Differential expression analysis of LCM RNA Data

Managing Packages Using Renv

To run this code in my project using the renv environment, run the following lines of code

install.packages("renv") #install the package on the new computer (may not be necessary if renv bootstraps itself as expected)
renv::restore() #reinstall all the package versions in the renv lockfile

Load packages

require("genefilter")
require("DESeq2")
require("apeglm")
require("ashr")
require("ggplot2")
require("vsn")
require("hexbin")
require("pheatmap")
require("RColorBrewer")
require("EnhancedVolcano")
require("rtracklayer")
require("tidyverse")

sessionInfo() #provides list of loaded packages and version of R.
## R version 4.3.2 (2023-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.0
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices datasets  utils     methods  
## [8] base     
## 
## other attached packages:
##  [1] lubridate_1.9.3             forcats_1.0.0              
##  [3] stringr_1.5.1               dplyr_1.1.4                
##  [5] purrr_1.0.2                 readr_2.1.5                
##  [7] tidyr_1.3.1                 tibble_3.2.1               
##  [9] tidyverse_2.0.0             rtracklayer_1.62.0         
## [11] EnhancedVolcano_1.18.0      ggrepel_0.9.6              
## [13] RColorBrewer_1.1-3          pheatmap_1.0.12            
## [15] hexbin_1.28.5               vsn_3.68.0                 
## [17] ggplot2_3.5.1               ashr_2.2-63                
## [19] apeglm_1.22.1               DESeq2_1.40.2              
## [21] SummarizedExperiment_1.30.2 Biobase_2.60.0             
## [23] MatrixGenerics_1.12.3       matrixStats_1.4.1          
## [25] GenomicRanges_1.54.1        GenomeInfoDb_1.36.4        
## [27] IRanges_2.34.1              S4Vectors_0.38.2           
## [29] BiocGenerics_0.46.0         genefilter_1.82.1          
## 
## loaded via a namespace (and not attached):
##  [1] DBI_1.2.3                bitops_1.0-9             rlang_1.1.4             
##  [4] magrittr_2.0.3           compiler_4.3.2           RSQLite_2.3.9           
##  [7] png_0.1-8                vctrs_0.6.5              pkgconfig_2.0.3         
## [10] crayon_1.5.3             fastmap_1.2.0            XVector_0.40.0          
## [13] utf8_1.2.4               Rsamtools_2.18.0         rmarkdown_2.28          
## [16] tzdb_0.4.0               preprocessCore_1.62.1    bit_4.5.0               
## [19] xfun_0.48                zlibbioc_1.46.0          cachem_1.1.0            
## [22] jsonlite_1.8.9           blob_1.2.4               DelayedArray_0.26.7     
## [25] BiocParallel_1.34.2      irlba_2.3.5.1            parallel_4.3.2          
## [28] R6_2.5.1                 stringi_1.8.4            bslib_0.8.0             
## [31] limma_3.56.2             SQUAREM_2021.1           jquerylib_0.1.4         
## [34] numDeriv_2016.8-1.1      Rcpp_1.0.13-1            knitr_1.48              
## [37] timechange_0.3.0         Matrix_1.6-5             splines_4.3.2           
## [40] tidyselect_1.2.1         rstudioapi_0.17.0        abind_1.4-8             
## [43] yaml_2.3.10              codetools_0.2-20         affy_1.78.2             
## [46] lattice_0.22-6           plyr_1.8.9               withr_3.0.1             
## [49] KEGGREST_1.40.1          coda_0.19-4.1            evaluate_1.0.1          
## [52] survival_3.7-0           Biostrings_2.70.3        pillar_1.9.0            
## [55] affyio_1.70.0            BiocManager_1.30.25      renv_1.0.11             
## [58] generics_0.1.3           invgamma_1.1             RCurl_1.98-1.16         
## [61] truncnorm_1.0-9          emdbook_1.3.13           hms_1.1.3               
## [64] munsell_0.5.1            scales_1.3.0             xtable_1.8-4            
## [67] glue_1.8.0               tools_4.3.2              BiocIO_1.12.0           
## [70] GenomicAlignments_1.38.2 annotate_1.78.0          locfit_1.5-9.10         
## [73] mvtnorm_1.3-2            XML_3.99-0.17            grid_4.3.2              
## [76] bbmle_1.0.25.1           bdsmatrix_1.3-7          AnnotationDbi_1.64.1    
## [79] colorspace_2.1-1         GenomeInfoDbData_1.2.10  restfulr_0.0.15         
## [82] cli_3.6.3                fansi_1.0.6              mixsqp_0.3-54           
## [85] S4Arrays_1.0.6           gtable_0.3.5             sass_0.4.9              
## [88] digest_0.6.37            SparseArray_1.2.4        rjson_0.2.23            
## [91] memoise_2.0.1            htmltools_0.5.8.1        lifecycle_1.0.4         
## [94] httr_1.4.7               bit64_4.5.2              MASS_7.3-60.0.1
#set standard output directory for figures
outdir <- "../output_RNA/differential_expression"

save_ggplot <- function(plot, filename, width = 10, height = 7, units = "in", dpi = 300) {
  print(plot)

  png_path <- file.path(outdir, paste0(filename, ".png"))
  pdf_dir <- file.path(outdir, "pdf_figs")
  pdf_path <- file.path(pdf_dir, paste0(filename, ".pdf"))
  
  # Ensure the pdf_figs directory exists
  if (!dir.exists(pdf_dir)) dir.create(pdf_dir, recursive = TRUE)
  
  # Save plots
  ggsave(filename = png_path, plot = plot, width = width, height = height, units = units, dpi = dpi)
  ggsave(filename = pdf_path, plot = plot, width = width, height = height, units = units, dpi = dpi)
}

# Specify colors
ann_colors = list(Tissue = c(OralEpi = "palegreen3" ,Aboral = "mediumpurple1"))

Read and clean count matrix and metadata

Read in raw count data

counts_raw <- read.csv("../output_RNA/stringtie-GeneExt/LCM_RNA_gene_count_matrix.csv", row.names = 1) #load in data

gene_id,LCM_15,LCM_16,LCM_20,LCM_21,LCM_26,LCM_27,LCM_4,LCM_5,LCM_8,LCM_9

Read in metadata

meta <- read.csv("../data_RNA/LCM_RNA_metadata.csv") %>%
            dplyr::arrange(Sample) %>%
            mutate(across(c(Tissue, Fragment, Section_Date, LCM_Date), factor)) # Set variables as factors 

meta$Tissue <- factor(meta$Tissue, levels = c("OralEpi","Aboral")) #we want OralEpi to be the baseline

Read in gff for gene coordinates

gff <- import("../references/Pocillopora_acuta_HIv2.genes.gff3")
gff_transcripts <- as.data.frame(gff) %>% filter(type == "transcript") %>%
                                          select(c(seqnames,start,end,width,strand,ID)) %>%
                                          dplyr::rename("chromosome"=seqnames, "query"=ID)

Data sanity checks!

stopifnot(all(meta$Sample %in% colnames(counts_raw))) #are all of the sample names in the metadata column names in the gene count matrix?
stopifnot(all(meta$Sample == colnames(counts_raw))) #are they the same in the same order?

pOverA filtering to reduce dataset

ffun<-filterfun(pOverA(0.5,10))  # Keep genes expressed at 10+ counts in at least 50% of samples
counts_filt_poa <- genefilter((counts_raw), ffun) #apply filter

filtered_counts <- counts_raw[counts_filt_poa,] #keep only rows that passed filter

paste0("Number of genes after filtering: ", sum(counts_filt_poa))
## [1] "Number of genes after filtering: 14464"
write.csv(filtered_counts, file = file.path(outdir, "filtered_counts.csv"))

There are now 14464 genes in the filtered dataset.

Data sanity checks:

all(meta$Sample %in% colnames(filtered_counts)) #are all of the sample names in the metadata column names in the gene count matrix?
## [1] TRUE
all(meta$Sample == colnames(filtered_counts))  #are they the same in the same order? Should be TRUE
## [1] TRUE

DESeq2

Create DESeq object and run DESeq2

dds <- DESeqDataSetFromMatrix(countData = filtered_counts,
                              colData = meta,
                              design= ~ Fragment + Tissue)

dds <- DESeq(dds)

### Extract results for Aboral vs. OralEpi contrast

res <- results(dds, contrast = c("Tissue","Aboral","OralEpi"))
resLFC <- lfcShrink(dds, coef="Tissue_Aboral_vs_OralEpi", res=res, type = "apeglm")

Extract results for adjusted p-value < 0.05

res <- resLFC

resOrdered <- res[order(res$pvalue),]# save differentially expressed genes

DE_05 <- as.data.frame(resOrdered) %>% filter(padj < 0.05)
DE_05_Up <- DE_05 %>% filter(log2FoldChange > 0) #Higher in Aboral, Lower in OralEpi
DE_05_Down <- DE_05 %>% filter(log2FoldChange < 0) #Lower in Aboral, Higher in OralEpi

nrow(DE_05)
## [1] 3606
nrow(DE_05_Up) #Higher in Aboral, Lower in OralEpi
## [1] 804
nrow(DE_05_Down) #Lower in Aboral, Higher in OralEpi
## [1] 2802
write.csv(as.data.frame(resOrdered), 
          file = file.path(outdir, "DESeq_results.csv"))

write.csv(DE_05, 
          file = file.path(outdir, "DEG_05.csv"))

Visualizing Differential Expression

EnhancedVolcano(resLFC, 
    lab = ifelse(resLFC$padj < 0.05, rownames(resLFC), ""),
    x = "log2FoldChange", 
    y = "pvalue"
)

Plots

plotMA(results(dds, contrast = c("Tissue","Aboral","OralEpi")), ylim=c(-20,20))

plotMA(resLFC, ylim=c(-20,20))

Log2 Fold Change Comparison

resultsNames(dds)
## [1] "Intercept"                "Fragment_B_vs_A"         
## [3] "Fragment_C_vs_A"          "Fragment_D_vs_A"         
## [5] "Fragment_E_vs_A"          "Tissue_Aboral_vs_OralEpi"
# because we are interested in the comparison and not the intercept, we set 'coef=2'
resNorm <- lfcShrink(dds, coef="Tissue_Aboral_vs_OralEpi", type="normal")
resAsh <- lfcShrink(dds, coef="Tissue_Aboral_vs_OralEpi", type="ashr")

par(mfrow=c(1,3), mar=c(4,4,2,1))
xlim <- c(1,1e5); ylim <- c(-20,20)
plotMA(resLFC, xlim=xlim, ylim=ylim, main="apeglm")
plotMA(resNorm, xlim=xlim, ylim=ylim, main="normal")
plotMA(resAsh, xlim=xlim, ylim=ylim, main="ashr")

plotCounts(dds, gene=which.max(res$log2FoldChange), intgroup="Tissue")

plotCounts(dds, gene=which.min(res$log2FoldChange), intgroup="Tissue")

Transforming count data for visualization

vsd <- vst(dds, blind=FALSE)
rld <- rlog(dds, blind=FALSE)
ntd <- normTransform(dds) # this gives log2(n + 1)

meanSdPlot(assay(vsd), main = "vsd")

meanSdPlot(assay(rld))

meanSdPlot(assay(ntd))

#save the vsd transformation
vsd_mat <- assay(vsd)
write.csv(vsd_mat, file = file.path(outdir, "vsd_expression_matrix.csv"))

Will move forward with vst transformation for visualizations

Heatmap of count matrix

heatmap_metadata <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])

#view all genes
pheatmap(assay(vsd), cluster_rows=TRUE, show_rownames=FALSE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)),
         annotation_colors = ann_colors,color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200))

#view highest count genes
select <- order(rowMeans(counts(dds,normalized=TRUE)),
                decreasing=TRUE)[1:20]

pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)),
         annotation_colors = ann_colors, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200))

#view most significantly differentially expressed genes

select <- order(res$padj)[1:20]

pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2, annotation_col=(heatmap_metadata%>% select(Tissue)),
         annotation_colors = ann_colors,color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200))

Heatmap of the sample-to-sample distances

sampleDists <- dist(t(assay(vsd)))

sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsd$Tissue, vsd$Fragment, sep="-")
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
         clustering_distance_rows=sampleDists,
         clustering_distance_cols=sampleDists,
         col=colors)

Principal component plot of the samples

pcaData <- plotPCA(vsd, intgroup=c("Tissue", "Fragment"), returnData=TRUE, ntop = 14464)

percentVar <- round(100 * attr(pcaData, "percentVar"))
PCA <- ggplot(pcaData, aes(PC1, PC2, color=Tissue, shape=Fragment)) +
  geom_point(size=2) +
  scale_color_manual(values = c("Aboral" = "mediumpurple1", "OralEpi" = "palegreen3"))+
  xlab(paste0("PC1: ",percentVar[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar[2],"% variance")) + 
  coord_fixed() + theme_bw()

save_ggplot(PCA, "PCA_allgenes")

PCA_small <- ggplot(pcaData, aes(PC1, PC2, color=Tissue)) +
  geom_point(size=2) +
  scale_color_manual(values = c("Aboral" = "mediumpurple1", "OralEpi" = "palegreen3"))+
  xlab(paste0("PC1: ",percentVar[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar[2],"% variance")) + 
  coord_fixed() + theme_bw()

ggsave(filename = paste0(outdir,"/PCA_allgenes_small", ".png"), plot = PCA_small, width = 4, height = 2.5, units = "in", dpi = 300)
pcaData <- plotPCA(vsd, intgroup=c("Tissue", "Fragment"), returnData=TRUE)

percentVar <- round(100 * attr(pcaData, "percentVar"))
PCA <- ggplot(pcaData, aes(PC1, PC2, color=Tissue, shape=Fragment)) +
  geom_point(size=2) +
  scale_color_manual(values = c("Aboral" = "mediumpurple1", "OralEpi" = "palegreen3"))+
  xlab(paste0("PC1: ",percentVar[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar[2],"% variance")) + 
  coord_fixed() + theme_bw()

save_ggplot(PCA, "PCA")

PCA_small <- ggplot(pcaData, aes(PC1, PC2, color=Tissue)) +
  geom_point(size=2) +
  scale_color_manual(values = c("Aboral" = "mediumpurple1", "OralEpi" = "palegreen3"))+
  xlab(paste0("PC1: ",percentVar[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar[2],"% variance")) + 
  coord_fixed() + theme_bw()

ggsave(filename = paste0(outdir,"/PCA_small", ".png"), plot = PCA_small, width = 4, height = 2.5, units = "in", dpi = 300)

Clearly, the majority of the variance in the data is explained by tissue type!

Annotation data

Download annotation files from genome website


# wget files
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.Conserved_Domain_Search_results.txt.gz

wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.EggNog_results.txt.gz

wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.KEGG_results.txt.gz

# move to references direcotry
mv *gz ../references

# unzip files
gunzip ../references/*gz
EggNog <- read.delim("../references/Pocillopora_acuta_HIv2.genes.EggNog_results.txt") %>% dplyr::rename("query" = X.query)

CDSearch <- read.delim("../references/Pocillopora_acuta_HIv2.genes.Conserved_Domain_Search_results.txt", quote = "") %>% dplyr::rename("query" = X.Query)

KEGG <- read.delim("../references/Pocillopora_acuta_HIv2.genes.KEGG_results.txt", header = FALSE) %>% dplyr::rename("query" = V1, "KeggTerm" = V2)
DE_05$query <- rownames(DE_05)
DE_05_annot <- DE_05 %>% left_join(CDSearch) %>% select(query,everything())
DE_05_eggnog <- DE_05 %>% left_join(EggNog) %>% select(query,everything())

write.csv(as.data.frame(DE_05_eggnog), file=paste0(outdir,"/DE_05_eggnog_annotation.csv"))

annot_all <- as.data.frame(rownames(dds)) %>% dplyr::rename("query" = `rownames(dds)`) %>% left_join(CDSearch)

eggnog_all <- as.data.frame(rownames(dds)) %>% dplyr::rename("query" = `rownames(dds)`) %>% left_join(EggNog)
gene_labels <- eggnog_all %>% select(query,PFAMs) %>%
  mutate_all(~ ifelse(is.na(.), "", .)) %>% #replace NAs with "" for labelling purposes
  separate(PFAMs, into = c("PFAMs", "rest of name"), sep = ",(?=.*?,)", extra = "merge")
  
#view most significantly differentially expressed genes

select <- order(res$padj)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
top50_DE
save_ggplot(top50_DE, "top50_DE")

#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
up_Aboral
save_ggplot(up_Aboral, "up_Aboral")

#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
up_OralEpi
save_ggplot(up_OralEpi, "up_OralEpi")

Genes of Interest

Single cell marker genes (Levy et al 2021)

MarkerGenes <- read.csv("../references/Pacuta_MarkerGenes_Levy2021.csv") %>% dplyr::rename("query" = 1, "List" = 2, "definition" = 3) %>% filter(List !="Toolkit")

MarkerGenes_broc <- read.csv("../output_RNA/marker_genes/Pacuta_Spis_Markers_pairs.csv") %>% select(protein_id_spB,cluster,Standardized_Name_spA ) %>% dplyr::rename("query" = 1, "List" = 2)

MarkerGenes$def_short <- ifelse(nchar(MarkerGenes$definition) > 20, 
                            paste0(substr(MarkerGenes$definition, 1, 17), "..."), 
                            MarkerGenes$definition)

Biomineralization toolkit

Biomin <- read.csv("../output_RNA/marker_genes/Pacuta_Biomin_Blast.csv") %>% dplyr::rename("query" = Pocillopora_acuta_best_hit) %>% select(-c(accessionnumber.geneID, Ref))
Biomin_broc <- read.csv("../output_RNA/marker_genes/Pacuta_Biomin_Spis_ortholog.csv") %>% dplyr::rename("query" = Pacuta_gene) %>% select(-c(X,accessionnumber_gene_id, ref))

Biomin <- Biomin %>%
  group_by(query,List) %>%
  summarize(definition = paste(unique(definition), collapse = ","))

Biomin$def_short <- ifelse(nchar(Biomin$definition) > 40, 
                            paste0(substr(Biomin$definition, 1, 37), "..."), 
                            Biomin$definition)

Biomin_filtered_counts <- filtered_counts[(rownames(filtered_counts) %in% Biomin$query),]

Biomin_broc <- Biomin_broc %>%
  group_by(query,List) %>%
  summarize(definition = paste(unique(definition), collapse = ","))

Biomin_broc$def_short <- ifelse(nchar(Biomin_broc$definition) > 40, 
                            paste0(substr(Biomin_broc$definition, 1, 37), "..."), 
                            Biomin_broc$definition)

Biomin_broc_filtered_counts <- filtered_counts[(rownames(filtered_counts) %in% Biomin_broc$query),]

write.csv(Biomin_filtered_counts, "../output_RNA/differential_expression/Biomin_filtered_counts.csv")

Additional gene lists

HoxGenes_Nvec <- read.csv("../output_RNA/marker_genes/Hox_nematostella.csv") %>% dplyr::rename("query" = Pacuta_gene) %>% select(-c(X))

HoxGenes_Nvec$def_short <- gsub("Homeobox protein", "Hox", HoxGenes_Nvec$Description, ignore.case = TRUE)

He_etal_Nvec <- read.csv("../output_RNA/marker_genes/He_etal_nematostella.csv") %>% dplyr::rename("query" = Pacuta_gene) %>% select(-c(X))

He_etal_Nvec$def_short <- gsub("Homeobox protein", "Hox", He_etal_Nvec$Description, ignore.case = TRUE)

DuBuc_etal_Nvec <- read.csv("../output_RNA/marker_genes/Wnt_nematostella.csv") %>% dplyr::rename("query" = Pacuta_gene) %>% select(-c(X))

HeatStressGenes <- read.csv("../output_RNA/marker_genes/HeatStressGenes_Pacuta.csv") %>% dplyr::rename("query" = Pacuta_gene) %>% select(-c(X))
DE_05$query <- rownames(DE_05)
resOrdered$query <- rownames(resOrdered)

join_genes_of_interest <- function(df, gene_set) {
  df %>%
    left_join(gene_set, by = "query") %>%
    select(query, everything()) %>%
    drop_na()
}

DE_05_biomin         <- join_genes_of_interest(DE_05, Biomin)
DE_05_Biomin_broc    <- join_genes_of_interest(DE_05, Biomin_broc)
DE_05_marker         <- join_genes_of_interest(DE_05, MarkerGenes)
DE_05_marker_broc    <- join_genes_of_interest(DE_05, MarkerGenes_broc)
DE_05_Hox            <- join_genes_of_interest(DE_05, HoxGenes_Nvec)
DE_05_He_etal        <- join_genes_of_interest(DE_05, He_etal_Nvec)
DE_05_DuBuc_etal     <- join_genes_of_interest(DE_05, DuBuc_etal_Nvec)

DESeq_biomin         <- join_genes_of_interest(as.data.frame(resOrdered), Biomin)
DESeq_Biomin_broc    <- join_genes_of_interest(as.data.frame(resOrdered), Biomin_broc)
DESeq_marker         <- join_genes_of_interest(as.data.frame(resOrdered), MarkerGenes)
DESeq_marker_broc    <- join_genes_of_interest(as.data.frame(resOrdered), MarkerGenes_broc)
DESeq_Hox            <- join_genes_of_interest(as.data.frame(resOrdered), HoxGenes_Nvec)
DESeq_He_etal        <- join_genes_of_interest(as.data.frame(resOrdered), He_etal_Nvec)
DESeq_DuBuc_etal     <- join_genes_of_interest(as.data.frame(resOrdered), DuBuc_etal_Nvec)

DESeq_Hox <- HoxGenes_Nvec %>% left_join(as.data.frame(resOrdered), by="query") %>% select(query,everything()) %>% drop_na()
DE_05_HeatStressGenes <- HeatStressGenes %>% left_join(DE_05, by="query") %>% select(query,everything()) %>% drop_na(baseMean)
DESeq_HeatStressGenes <- HeatStressGenes %>% left_join(as.data.frame(resOrdered), by="query") %>% select(query,everything())  %>% drop_na(baseMean)

write.csv(as.data.frame(DE_05_biomin), file=paste0(outdir,"/DE_05_biomin_annotation.csv"))
write.csv(as.data.frame(DE_05_marker), file=paste0(outdir,"/DE_05_markergene_annotation.csv"))

biomin_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(Biomin) 
biomin_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(Biomin) 

Biomin_broc_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(Biomin_broc) 
Biomin_broc_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(Biomin_broc) 

markers_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(MarkerGenes) 
markers_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(MarkerGenes) 

broc_markers_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(MarkerGenes_broc) 
broc_markers_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(MarkerGenes_broc) 
#view biomin genes that are differentially expressed

z_scores <- t(scale(t(assay(vsd)[DE_05_biomin$query, ]), center = TRUE, scale = TRUE))
DE_biomin <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = DE_05_biomin$def_short, fontsize_row = 5)
DE_biomin
save_ggplot(DE_biomin, "DE_biomin")

#view biomin genes that are differentially expressed

z_scores <- t(scale(t(assay(vsd)[DE_05_Biomin_broc$query, ]), center = TRUE, scale = TRUE))
DE_Biomin_broc <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = DE_05_Biomin_broc$def_short, fontsize_row = 5)
DE_Biomin_broc
save_ggplot(DE_Biomin_broc, "DE_Biomin_broc")

#view marker genes that are differentially expressed

z_scores <- t(scale(t(assay(vsd)[DE_05_marker$query, ]), center = TRUE, scale = TRUE))
DE_marker <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,cutree_rows = 5,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = DE_05_marker$List, fontsize_row = 4)
DE_marker
save_ggplot(DE_marker, "DE_marker")

DE_05_marker_grouped <- DE_05_marker %>% arrange(List) %>% mutate(List = as.factor(List))

z_scores <- t(scale(t(assay(vsd)[DE_05_marker_grouped$quer, ]), center = TRUE, scale = TRUE))
DE_05_marker_grouped_plot <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = DE_05_marker_grouped$List, fontsize_row = 5)

DE_05_marker_grouped_plot
save_ggplot(DE_05_marker_grouped_plot, "DE_05_marker_grouped")

#view marker genes that are differentially expressed

z_scores <- t(scale(t(assay(vsd)[DE_05_marker_broc$query, ]), center = TRUE, scale = TRUE))
DE_marker <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,cutree_rows = 5,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = DE_05_marker_broc$List, fontsize_row = 4)
DE_marker
save_ggplot(DE_marker, "DE_marker_broc")

DE_05_marker_broc_grouped <- DE_05_marker_broc %>% arrange(List) %>% mutate(List = as.factor(List))

z_scores <- t(scale(t(assay(vsd)[DE_05_marker_broc_grouped$quer, ]), center = TRUE, scale = TRUE))
DE_05_marker_broc_grouped_plot <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = DE_05_marker_broc_grouped$List, fontsize_row = 5)

DE_05_marker_broc_grouped_plot
save_ggplot(DE_05_marker_broc_grouped_plot, "DE_05_marker_broc_grouped")

Visualizing Differential Expression

Biomin_volcano <- EnhancedVolcano(biomin_all_res, 
    lab = biomin_all_res$def_short,
    x = 'log2FoldChange',
    y = 'padj',
    pCutoff = 0.01,
    drawConnectors = TRUE,
    widthConnectors = 0.75,
    pointSize = 1,
    labSize = 2,boxedLabels = TRUE,max.overlaps = 40)

save_ggplot(Biomin_volcano, "Biomin_volcano")

Biomin_broc_volcano <- EnhancedVolcano(Biomin_broc_all_res, 
    lab = Biomin_broc_all_res$def_short,
    x = 'log2FoldChange',
    y = 'padj',
    pCutoff = 0.01,
    drawConnectors = TRUE,
    widthConnectors = 0.75,
    pointSize = 1,
    labSize = 2,boxedLabels = TRUE,max.overlaps = 40)

save_ggplot(Biomin_broc_volcano, "Biomin_broc_volcano")

Marker_volcano <- EnhancedVolcano(markers_all_res, 
    lab = markers_all_res$List,
    x = 'log2FoldChange',
    y = 'padj',
    pCutoff = 0.01,
    drawConnectors = TRUE,
    widthConnectors = 0.75,
    pointSize = 1,
    labSize = 2,boxedLabels = TRUE,max.overlaps = 60)

save_ggplot(Marker_volcano, "Marker_volcano")

Marker_volcano_names <- EnhancedVolcano(markers_all_res, 
    lab = markers_all_res$def_short,
    x = 'log2FoldChange',
    y = 'padj',
    pCutoff = 0.01,
    drawConnectors = TRUE,
    widthConnectors = 0.75,
    pointSize = 1,
    labSize = 2,boxedLabels = TRUE,max.overlaps = 60)

save_ggplot(Marker_volcano_names, "Marker_volcano_names")

Marker_volcano <- EnhancedVolcano(broc_markers_all_res, 
    lab = broc_markers_all_res$List,
    x = 'log2FoldChange',
    y = 'padj',
    pCutoff = 0.01,
    drawConnectors = TRUE,
    widthConnectors = 0.75,
    pointSize = 1,
    labSize = 2,boxedLabels = TRUE,max.overlaps = 60)

save_ggplot(Marker_volcano, "Marker_volcano_broc")

EnhancedVolcano(resLFC, 
    lab = rownames(resLFC),
    x = 'log2FoldChange',
    y = 'pvalue')

volcano_plain <- EnhancedVolcano(resLFC, 
    lab = NA,
    x = 'log2FoldChange',
    y = 'padj',
    pCutoff = 0.05,
    title="",
    subtitle="",
    drawConnectors = TRUE,
    widthConnectors = 0.75,
    pointSize = 1,
    labSize = 2,boxedLabels = TRUE,max.overlaps = 60)

save_ggplot(volcano_plain, "volcano_plain",width = 4, height = 6, units = "in", dpi = 300)

Trying to annotate un-annotated DE genes:

# wget protein sequence reference file
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.pep.faa.gz

# move to references direcotry
mv *gz ../references

# unzip files
gunzip ../references/*gz

#get the names of all the DEGs from the first column of the DEG csv file
tail -n +2 ../output_RNA/differential_expression/DEG_05.csv | cut -d',' -f1 | tr -d '"' > ../output_RNA/differential_expression/DEG_05_names.csv

#grep this file against the protein fasta file, first with wc -l to make sure the number of lines is correct (should be your number of DEGs)
grep -f ../output_RNA/differential_expression/DEG_05_names.csv ../references/Pocillopora_acuta_HIv2.genes.pep.faa | wc -l

#grep each header with the protein sequence after ("-A 1") and save to a new file
grep  -A 1 -f ../output_RNA/differential_expression/DEG_05_names.csv ../references/Pocillopora_acuta_HIv2.genes.pep.faa > ../output_RNA/differential_expression/DEG_05_seqs.txt

nr database BLAST of DE_05 genes only

On andromeda:

Blastp-ing only the DE genes against the entire nr database (will take a while)

cd ../scripts
nano DEG_05_blast.sh
#!/bin/bash
#SBATCH --job-name="DE_blast"
#SBATCH -t 240:00:00
#SBATCH --export=NONE
#SBATCH --mail-type=BEGIN,END,FAIL #email you when job starts, stops and/or fails
#SBATCH --mail-user=zdellaert@uri.edu #your email to send notifications
#SBATCH --mem=500GB
#SBATCH --error=../scripts/outs_errs/"%x_error.%j" #write out slurm error reports
#SBATCH --output=../scripts/outs_errs/"%x_output.%j" #write out any program outpus
#SBATCH --account=putnamlab
#SBATCH --nodes=2 --ntasks-per-node=24

module load BLAST+/2.15.0-gompi-2023a

cd ../output_RNA/differential_expression #set working directory
mkdir blast
cd blast

#nr database location andromeda: /data/shared/ncbi-db/.ncbirc 
# points to current location: cat /data/shared/ncbi-db/.ncbirc
# [BLAST]
# BLASTDB=/data/shared/ncbi-db/2024-11-10

blastp -query ../DEG_05_seqs.txt -db nr -out DEG_05_blast_results.txt -outfmt 0 -evalue 1E-05 \
-num_threads 48 \
-max_target_seqs 10

blastp -query ../DEG_05_seqs.txt -db nr -out DEG_05_blast_results_tab.txt -outfmt 6 -evalue 1E-05 \
-num_threads 48 \
-max_target_seqs 1 \
-max_hsps 1

echo "Blast complete!" $(date)
sbatch DEG_05_blast.sh
cd ../output_RNA/differential_expression/blast

wc -l DEG_05_blast_results_tab.txt #3537 of the 3606 genes were annotated

#get just the NCBI database accession numbers for the blast results
cut -f2 DEG_05_blast_results_tab.txt > DEG_05_blast_accessions.txt

#remove any duplicates
sort -u  DEG_05_blast_accessions.txt > unique_DEG_05_blast_accessions.txt

wc -l unique_DEG_05_blast_accessions.txt #3404 of the 3537 annotations were unique

while read acc; do
  curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=protein&id=$acc&rettype=gp&retmode=text" \
  | grep "DEFINITION" | sed 's/DEFINITION  //g' | awk -v id="$acc" '{print id "\t" $0}'
done < unique_DEG_05_blast_accessions.txt > DEG_05_blast_names.txt

wc -l DEG_05_blast_names.txt #3396 ; unsure why 8 are missing.

join -1 2 -2 1 -t $'\t' <(sort -k2 DEG_05_blast_results_tab.txt) <(sort DEG_05_blast_names.txt) > annotated_DEG_05_blast_results_tab.txt

SwissProt of ALL genes

On unity:

swissprot based on https://github.com/urol-e5/deep-dive/blob/main/D-Apul/code/20-Apul-gene-annotation.Rmd and https://github.com/urol-e5/deep-dive/blob/main/F-Pmea/code/20-Pmea-gene-annotation.Rmd

Steven’s notebook post here: https://sr320.github.io/tumbling-oysters/posts/sr320-27-go/

mkdir ../references/blast_dbs
cd ../references/blast_dbs
curl -O https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz
mv uniprot_sprot.fasta.gz uniprot_sprot_r2024_10_02.fasta.gz
gunzip -k uniprot_sprot_r2024_10_02.fasta.gz
rm uniprot_sprot_r2024_10_02.fasta.gz

head uniprot_sprot_r2024_10_02.fasta
echo "Number of Sequences"
grep -c ">" uniprot_sprot_r2024_10_02.fasta
# 572214 sequences
module load blast-plus/2.14.1

makeblastdb \
-in ../references/blast_dbs/uniprot_sprot_r2024_10_02.fasta \
-dbtype prot \
-out ../references/blast_dbs/uniprot_sprot_r2024_10_02
cd ../scripts
nano blastp_SwissProt.sh
#!/bin/bash
#SBATCH -t 18:00:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=48
#SBATCH --mem=500GB
#SBATCH --export=NONE
#SBATCH --error=../scripts/outs_errs/"%x_error.%j" #write out slurm error reports
#SBATCH --output=../scripts/outs_errs/"%x_output.%j" #write out any program outpus
#SBATCH --mail-type=BEGIN,END,FAIL #email you when job starts, stops and/or fails
#SBATCH -D /project/pi_hputnam_uri_edu/zdellaert/LaserCoral #set working directory

module load blast-plus/2.14.1

cd references/
mkdir annotation

fasta="Pocillopora_acuta_HIv2.genes.pep.faa"

blastp \
-query $fasta \
-db blast_dbs/uniprot_sprot_r2024_10_02 \
-out annotation/blastp_SwissProt_out.tab \
-evalue 1E-05 \
-num_threads 48 \
-max_target_seqs 1 \
-max_hsps 1 \
-outfmt 6

echo "Blast complete!" $(date)
cd references/annotation/

tr '|' '\t' <  blastp_SwissProt_out.tab >  blastp_SwissProt_out_sep.tab
cd ../references/annotation/

curl -H "Accept: text/plain; format=tsv" "https://rest.uniprot.org/uniprotkb/stream?fields=accession%2Creviewed%2Cid%2Cprotein_name%2Cgene_names%2Corganism_name%2Clength%2Cgo_p%2Cgo%2Cgo_id%2Cgo_c%2Cgo_f&format=tsv&query=%28reviewed%3Atrue%29" -o SwissProt-Annot-GO_111524.tsv

wc -l SwissProt-Annot-GO_111524.tsv
#572215

All code below based on https://github.com/urol-e5/deep-dive/blob/main/D-Apul/code/20-Apul-gene-annotation.Rmd and https://github.com/urol-e5/deep-dive/blob/main/F-Pmea/code/20-Pmea-gene-annotation.Rmd

Steven’s notebook post here: https://sr320.github.io/tumbling-oysters/posts/sr320-27-go/

bltabl <- read.csv("../references/annotation/blastp_SwissProt_out_sep.tab", sep = '\t', header = FALSE)

spgo <- read.csv("../references/annotation/SwissProt-Annot-GO_111524.tsv", sep = '\t', header = TRUE)
annot_tab <- left_join(bltabl, spgo, by = c("V3" = "Entry")) %>%
  select(
    query = V1,
    blast_hit = V3,
    evalue = V13,
    ProteinNames = Protein.names,
    BiologicalProcess = Gene.Ontology..biological.process.,
    GeneOntologyIDs = Gene.Ontology.IDs
  )

head(annot_tab)
##                                        query blast_hit    evalue
## 1 Pocillopora_acuta_HIv2___RNAseq.g24143.t1a    Q4JAI4  1.02e-37
## 2  Pocillopora_acuta_HIv2___RNAseq.g18333.t1    O08807 9.62e-116
## 3   Pocillopora_acuta_HIv2___RNAseq.g7985.t1    O74212 3.56e-158
## 4      Pocillopora_acuta_HIv2___TS.g15308.t1    Q09575  1.08e-12
## 5   Pocillopora_acuta_HIv2___RNAseq.g2057.t1    P0C1P0  8.81e-14
## 6   Pocillopora_acuta_HIv2___RNAseq.g4696.t1    Q9W2Q5  8.98e-69
##                                                                                                                                                                                                     ProteinNames
## 1                                                                                                                                              Methionine synthase (EC 2.1.1.-) (Homocysteine methyltransferase)
## 2 Peroxiredoxin-4 (EC 1.11.1.24) (Antioxidant enzyme AOE372) (Peroxiredoxin IV) (Prx-IV) (Thioredoxin peroxidase AO372) (Thioredoxin-dependent peroxide reductase A0372) (Thioredoxin-dependent peroxiredoxin 4)
## 3                                                                                                   Acyl-lipid (8-3)-desaturase (EC 1.14.19.30) (Delta(5) fatty acid desaturase) (Delta-5 fatty acid desaturase)
## 4                                                                                                                                                                                Uncharacterized protein K02A2.6
## 5                                                                              Phosphatidylinositol N-acetylglucosaminyltransferase subunit Y (Phosphatidylinositol-glycan biosynthesis class Y protein) (PIG-Y)
## 6                                                                                                                                                                   Calcium and integrin-binding family member 2
##                                                                                                                                                                                                                                                                                                                                                                                                                   BiologicalProcess
## 1                                                                                                                                                                                                                                                                                                                                                            methionine biosynthetic process [GO:0009086]; methylation [GO:0032259]
## 2 cell redox homeostasis [GO:0045454]; extracellular matrix organization [GO:0030198]; hydrogen peroxide catabolic process [GO:0042744]; male gonad development [GO:0008584]; negative regulation of male germ cell proliferation [GO:2000255]; protein maturation by protein folding [GO:0022417]; reactive oxygen species metabolic process [GO:0072593]; response to oxidative stress [GO:0006979]; spermatogenesis [GO:0007283]
## 3                                                                                                                                                                                                                                                                                                                 long-chain fatty acid biosynthetic process [GO:0042759]; unsaturated fatty acid biosynthetic process [GO:0006636]
## 4                                                                                                                                                                                                                                                                                                                                                                                                      DNA integration [GO:0015074]
## 5                                                                                                                                                                                                                                                                                                                                                                                      GPI anchor biosynthetic process [GO:0006506]
## 6                                                                                                                                                                                                                                                                                                                                                              calcium ion homeostasis [GO:0055074]; phototransduction [GO:0007602]
##                                                                                                                                                                                                          GeneOntologyIDs
## 1                                                                                                                                                                         GO:0003871; GO:0008270; GO:0009086; GO:0032259
## 2 GO:0005615; GO:0005737; GO:0005739; GO:0005783; GO:0005790; GO:0005829; GO:0006979; GO:0007283; GO:0008379; GO:0008584; GO:0022417; GO:0030198; GO:0042744; GO:0042802; GO:0045454; GO:0072593; GO:0140313; GO:2000255
## 3                                                                                                                                                 GO:0006636; GO:0016020; GO:0020037; GO:0042759; GO:0046872; GO:0102866
## 4                                                                                                                                                 GO:0003676; GO:0005737; GO:0008270; GO:0015074; GO:0019899; GO:0042575
## 5                                                                                                                                                                                                 GO:0000506; GO:0006506
## 6                                                                                                                                                             GO:0000287; GO:0005509; GO:0005737; GO:0007602; GO:0055074
write.table(annot_tab, 
            file = "../references/annotation/protein-GO.tsv", 
            sep = "\t", 
            row.names = FALSE, 
            quote = FALSE)

DE_05_SwissProt <- DE_05 %>% left_join(annot_tab) %>% select(query,everything()) 

write.csv(as.data.frame(DE_05_SwissProt), file=paste0(outdir,"/DE_05_SwissProt_annotation.csv"))
DE_05_SwissProt$short_name <- ifelse(nchar(DE_05_SwissProt$ProteinNames) > 30, 
                            paste0(substr(DE_05_SwissProt$ProteinNames, 1, 27), "..."), 
                            DE_05_SwissProt$ProteinNames)

gene_labels <- DE_05_SwissProt %>% 
  select(query,short_name) %>%
  mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes

#view most significantly differentially expressed genes

select <- order(res$padj)[1:50]
rownames(res)[select]
##  [1] "Pocillopora_acuta_HIv2___RNAseq.g6351.t1"  
##  [2] "Pocillopora_acuta_HIv2___RNAseq.g28575.t1a"
##  [3] "Pocillopora_acuta_HIv2___RNAseq.g27038.t1" 
##  [4] "Pocillopora_acuta_HIv2___RNAseq.g2165.t1"  
##  [5] "Pocillopora_acuta_HIv2___TS.g18104.t1"     
##  [6] "Pocillopora_acuta_HIv2___RNAseq.g14330.t3" 
##  [7] "Pocillopora_acuta_HIv2___RNAseq.g14025.t1" 
##  [8] "Pocillopora_acuta_HIv2___RNAseq.g14090.t1" 
##  [9] "Pocillopora_acuta_HIv2___RNAseq.g10378.t1" 
## [10] "Pocillopora_acuta_HIv2___RNAseq.g3832.t1"  
## [11] "Pocillopora_acuta_HIv2___RNAseq.g8006.t1"  
## [12] "Pocillopora_acuta_HIv2___RNAseq.g14253.t1" 
## [13] "Pocillopora_acuta_HIv2___RNAseq.g25431.t1" 
## [14] "Pocillopora_acuta_HIv2___RNAseq.11056_t"   
## [15] "Pocillopora_acuta_HIv2___RNAseq.g9588.t1"  
## [16] "Pocillopora_acuta_HIv2___TS.g30765.t1"     
## [17] "Pocillopora_acuta_HIv2___RNAseq.g14021.t1" 
## [18] "Pocillopora_acuta_HIv2___RNAseq.g19082.t1" 
## [19] "Pocillopora_acuta_HIv2___RNAseq.g16202.t1" 
## [20] "Pocillopora_acuta_HIv2___TS.g19991.t2"     
## [21] "Pocillopora_acuta_HIv2___RNAseq.g20860.t1" 
## [22] "Pocillopora_acuta_HIv2___RNAseq.g8588.t1"  
## [23] "Pocillopora_acuta_HIv2___RNAseq.g26604.t1" 
## [24] "Pocillopora_acuta_HIv2___TS.g18100.t1"     
## [25] "Pocillopora_acuta_HIv2___TS.g27642.t1b"    
## [26] "Pocillopora_acuta_HIv2___RNAseq.g11588.t1" 
## [27] "Pocillopora_acuta_HIv2___RNAseq.g25327.t1" 
## [28] "Pocillopora_acuta_HIv2___RNAseq.g12281.t1" 
## [29] "Pocillopora_acuta_HIv2___RNAseq.g19085.t1" 
## [30] "Pocillopora_acuta_HIv2___RNAseq.g19284.t1" 
## [31] "Pocillopora_acuta_HIv2___TS.g11360.t1"     
## [32] "Pocillopora_acuta_HIv2___RNAseq.g7803.t1"  
## [33] "Pocillopora_acuta_HIv2___TS.g16008.t2"     
## [34] "Pocillopora_acuta_HIv2___RNAseq.g22978.t3b"
## [35] "Pocillopora_acuta_HIv2___RNAseq.g2406.t1"  
## [36] "Pocillopora_acuta_HIv2___RNAseq.g8119.t1"  
## [37] "Pocillopora_acuta_HIv2___TS.g9414.t1"      
## [38] "Pocillopora_acuta_HIv2___RNAseq.g21374.t1" 
## [39] "Pocillopora_acuta_HIv2___RNAseq.g21000.t1" 
## [40] "Pocillopora_acuta_HIv2___RNAseq.g5252.t1"  
## [41] "Pocillopora_acuta_HIv2___RNAseq.g22853.t1" 
## [42] "Pocillopora_acuta_HIv2___RNAseq.g17117.t1" 
## [43] "Pocillopora_acuta_HIv2___RNAseq.g25759.t1" 
## [44] "Pocillopora_acuta_HIv2___TS.g16802.t1"     
## [45] "Pocillopora_acuta_HIv2___RNAseq.g24120.t1" 
## [46] "Pocillopora_acuta_HIv2___RNAseq.30415_t"   
## [47] "Pocillopora_acuta_HIv2___RNAseq.g14336.t1" 
## [48] "Pocillopora_acuta_HIv2___RNAseq.g26594.t2" 
## [49] "Pocillopora_acuta_HIv2___RNAseq.g19115.t1" 
## [50] "Pocillopora_acuta_HIv2___TS.g4983.t1"
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "top50_DE_SwissProt")

#view most significantly differentially expressed genes by LFC

select <- order(abs(res$log2FoldChange),decreasing = TRUE)[1:50]
rownames(res)[select]
##  [1] "Pocillopora_acuta_HIv2___RNAseq.g28575.t1a"
##  [2] "Pocillopora_acuta_HIv2___TS.g18104.t1"     
##  [3] "Pocillopora_acuta_HIv2___RNAseq.g27038.t1" 
##  [4] "Pocillopora_acuta_HIv2___RNAseq.g14025.t1" 
##  [5] "Pocillopora_acuta_HIv2___RNAseq.g2165.t1"  
##  [6] "Pocillopora_acuta_HIv2___RNAseq.g14330.t3" 
##  [7] "Pocillopora_acuta_HIv2___RNAseq.g14090.t1" 
##  [8] "Pocillopora_acuta_HIv2___TS.g30765.t1"     
##  [9] "Pocillopora_acuta_HIv2___RNAseq.g8006.t1"  
## [10] "Pocillopora_acuta_HIv2___RNAseq.g10378.t1" 
## [11] "Pocillopora_acuta_HIv2___RNAseq.g14253.t1" 
## [12] "Pocillopora_acuta_HIv2___RNAseq.11056_t"   
## [13] "Pocillopora_acuta_HIv2___RNAseq.g22261.t1" 
## [14] "Pocillopora_acuta_HIv2___RNAseq.g14021.t1" 
## [15] "Pocillopora_acuta_HIv2___RNAseq.g19082.t1" 
## [16] "Pocillopora_acuta_HIv2___TS.g19991.t2"     
## [17] "Pocillopora_acuta_HIv2___RNAseq.g19284.t1" 
## [18] "Pocillopora_acuta_HIv2___TS.g4983.t1"      
## [19] "Pocillopora_acuta_HIv2___RNAseq.g21000.t1" 
## [20] "Pocillopora_acuta_HIv2___RNAseq.g20860.t1" 
## [21] "Pocillopora_acuta_HIv2___TS.g9414.t1"      
## [22] "Pocillopora_acuta_HIv2___RNAseq.g8588.t1"  
## [23] "Pocillopora_acuta_HIv2___RNAseq.g11588.t1" 
## [24] "Pocillopora_acuta_HIv2___RNAseq.g12281.t1" 
## [25] "Pocillopora_acuta_HIv2___TS.g27642.t1b"    
## [26] "Pocillopora_acuta_HIv2___RNAseq.g7803.t1"  
## [27] "Pocillopora_acuta_HIv2___RNAseq.g8119.t1"  
## [28] "Pocillopora_acuta_HIv2___RNAseq.g17117.t1" 
## [29] "Pocillopora_acuta_HIv2___RNAseq.g22978.t3b"
## [30] "Pocillopora_acuta_HIv2___RNAseq.30415_t"   
## [31] "Pocillopora_acuta_HIv2___TS.g16008.t2"     
## [32] "Pocillopora_acuta_HIv2___RNAseq.g2406.t1"  
## [33] "Pocillopora_acuta_HIv2___RNAseq.g25759.t1" 
## [34] "Pocillopora_acuta_HIv2___RNAseq.g24120.t1" 
## [35] "Pocillopora_acuta_HIv2___RNAseq.g14336.t1" 
## [36] "Pocillopora_acuta_HIv2___RNAseq.g9631.t1"  
## [37] "Pocillopora_acuta_HIv2___RNAseq.g7627.t1"  
## [38] "Pocillopora_acuta_HIv2___RNAseq.g8062.t1"  
## [39] "Pocillopora_acuta_HIv2___RNAseq.g1102.t1"  
## [40] "Pocillopora_acuta_HIv2___RNAseq.g14484.t1" 
## [41] "Pocillopora_acuta_HIv2___RNAseq.g21373.t1" 
## [42] "Pocillopora_acuta_HIv2___TS.g16384.t1"     
## [43] "Pocillopora_acuta_HIv2___RNAseq.g13561.t1" 
## [44] "Pocillopora_acuta_HIv2___RNAseq.g1126.t1"  
## [45] "Pocillopora_acuta_HIv2___RNAseq.g9210.t1"  
## [46] "Pocillopora_acuta_HIv2___RNAseq.g24649.t1" 
## [47] "Pocillopora_acuta_HIv2___RNAseq.g11659.t1" 
## [48] "Pocillopora_acuta_HIv2___TS.g25577.t1a"    
## [49] "Pocillopora_acuta_HIv2___RNAseq.g27681.t1b"
## [50] "Pocillopora_acuta_HIv2___TS.g1968.t2"
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "top50_LFC_DE_SwissProt")

#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_Aboral
save_ggplot(up_Aboral, "up_Aboral_SwissProt")

#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_OralEpi
save_ggplot(up_OralEpi, "up_OralEpi_SwissProt")

DE_05_SwissProt$short_GO <- ifelse(nchar(DE_05_SwissProt$BiologicalProcess) > 30, 
                            paste0(substr(DE_05_SwissProt$BiologicalProcess, 1, 27), "..."), 
                            DE_05_SwissProt$BiologicalProcess)

gene_labels <- DE_05_SwissProt %>% select(query,short_GO) %>%
  mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes

#view most significantly differentially expressed genes

select <- order(res$padj)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
top50_DE
save_ggplot(top50_DE, "top50_DE_Blast2GO")

#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_Aboral
save_ggplot(up_Aboral, "up_Aboral_Blast2GO")

#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_OralEpi
save_ggplot(up_OralEpi, "up_OralEpi_Blast2GO")

Combined annotations manually

Manual <- read.csv(paste0(outdir,"/DE_05_Manual_annotation.csv")) %>% dplyr::rename("query" = 2, "definition" = 3) %>% arrange(X) 

gene_labels <- Manual %>% 
  select(query,Heatmap_Label) %>%
  mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
#view most significantly differentially expressed genes

select <- order(res$padj)[1:50]
rownames(res)[select]
##  [1] "Pocillopora_acuta_HIv2___RNAseq.g6351.t1"  
##  [2] "Pocillopora_acuta_HIv2___RNAseq.g28575.t1a"
##  [3] "Pocillopora_acuta_HIv2___RNAseq.g27038.t1" 
##  [4] "Pocillopora_acuta_HIv2___RNAseq.g2165.t1"  
##  [5] "Pocillopora_acuta_HIv2___TS.g18104.t1"     
##  [6] "Pocillopora_acuta_HIv2___RNAseq.g14330.t3" 
##  [7] "Pocillopora_acuta_HIv2___RNAseq.g14025.t1" 
##  [8] "Pocillopora_acuta_HIv2___RNAseq.g14090.t1" 
##  [9] "Pocillopora_acuta_HIv2___RNAseq.g10378.t1" 
## [10] "Pocillopora_acuta_HIv2___RNAseq.g3832.t1"  
## [11] "Pocillopora_acuta_HIv2___RNAseq.g8006.t1"  
## [12] "Pocillopora_acuta_HIv2___RNAseq.g14253.t1" 
## [13] "Pocillopora_acuta_HIv2___RNAseq.g25431.t1" 
## [14] "Pocillopora_acuta_HIv2___RNAseq.11056_t"   
## [15] "Pocillopora_acuta_HIv2___RNAseq.g9588.t1"  
## [16] "Pocillopora_acuta_HIv2___TS.g30765.t1"     
## [17] "Pocillopora_acuta_HIv2___RNAseq.g14021.t1" 
## [18] "Pocillopora_acuta_HIv2___RNAseq.g19082.t1" 
## [19] "Pocillopora_acuta_HIv2___RNAseq.g16202.t1" 
## [20] "Pocillopora_acuta_HIv2___TS.g19991.t2"     
## [21] "Pocillopora_acuta_HIv2___RNAseq.g20860.t1" 
## [22] "Pocillopora_acuta_HIv2___RNAseq.g8588.t1"  
## [23] "Pocillopora_acuta_HIv2___RNAseq.g26604.t1" 
## [24] "Pocillopora_acuta_HIv2___TS.g18100.t1"     
## [25] "Pocillopora_acuta_HIv2___TS.g27642.t1b"    
## [26] "Pocillopora_acuta_HIv2___RNAseq.g11588.t1" 
## [27] "Pocillopora_acuta_HIv2___RNAseq.g25327.t1" 
## [28] "Pocillopora_acuta_HIv2___RNAseq.g12281.t1" 
## [29] "Pocillopora_acuta_HIv2___RNAseq.g19085.t1" 
## [30] "Pocillopora_acuta_HIv2___RNAseq.g19284.t1" 
## [31] "Pocillopora_acuta_HIv2___TS.g11360.t1"     
## [32] "Pocillopora_acuta_HIv2___RNAseq.g7803.t1"  
## [33] "Pocillopora_acuta_HIv2___TS.g16008.t2"     
## [34] "Pocillopora_acuta_HIv2___RNAseq.g22978.t3b"
## [35] "Pocillopora_acuta_HIv2___RNAseq.g2406.t1"  
## [36] "Pocillopora_acuta_HIv2___RNAseq.g8119.t1"  
## [37] "Pocillopora_acuta_HIv2___TS.g9414.t1"      
## [38] "Pocillopora_acuta_HIv2___RNAseq.g21374.t1" 
## [39] "Pocillopora_acuta_HIv2___RNAseq.g21000.t1" 
## [40] "Pocillopora_acuta_HIv2___RNAseq.g5252.t1"  
## [41] "Pocillopora_acuta_HIv2___RNAseq.g22853.t1" 
## [42] "Pocillopora_acuta_HIv2___RNAseq.g17117.t1" 
## [43] "Pocillopora_acuta_HIv2___RNAseq.g25759.t1" 
## [44] "Pocillopora_acuta_HIv2___TS.g16802.t1"     
## [45] "Pocillopora_acuta_HIv2___RNAseq.g24120.t1" 
## [46] "Pocillopora_acuta_HIv2___RNAseq.30415_t"   
## [47] "Pocillopora_acuta_HIv2___RNAseq.g14336.t1" 
## [48] "Pocillopora_acuta_HIv2___RNAseq.g26594.t2" 
## [49] "Pocillopora_acuta_HIv2___RNAseq.g19115.t1" 
## [50] "Pocillopora_acuta_HIv2___TS.g4983.t1"
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "top50_DE_Manual")

#view most significantly differentially expressed genes by LFC

select <- order(abs(res$log2FoldChange),decreasing = TRUE)[1:50]
rownames(res)[select]
##  [1] "Pocillopora_acuta_HIv2___RNAseq.g28575.t1a"
##  [2] "Pocillopora_acuta_HIv2___TS.g18104.t1"     
##  [3] "Pocillopora_acuta_HIv2___RNAseq.g27038.t1" 
##  [4] "Pocillopora_acuta_HIv2___RNAseq.g14025.t1" 
##  [5] "Pocillopora_acuta_HIv2___RNAseq.g2165.t1"  
##  [6] "Pocillopora_acuta_HIv2___RNAseq.g14330.t3" 
##  [7] "Pocillopora_acuta_HIv2___RNAseq.g14090.t1" 
##  [8] "Pocillopora_acuta_HIv2___TS.g30765.t1"     
##  [9] "Pocillopora_acuta_HIv2___RNAseq.g8006.t1"  
## [10] "Pocillopora_acuta_HIv2___RNAseq.g10378.t1" 
## [11] "Pocillopora_acuta_HIv2___RNAseq.g14253.t1" 
## [12] "Pocillopora_acuta_HIv2___RNAseq.11056_t"   
## [13] "Pocillopora_acuta_HIv2___RNAseq.g22261.t1" 
## [14] "Pocillopora_acuta_HIv2___RNAseq.g14021.t1" 
## [15] "Pocillopora_acuta_HIv2___RNAseq.g19082.t1" 
## [16] "Pocillopora_acuta_HIv2___TS.g19991.t2"     
## [17] "Pocillopora_acuta_HIv2___RNAseq.g19284.t1" 
## [18] "Pocillopora_acuta_HIv2___TS.g4983.t1"      
## [19] "Pocillopora_acuta_HIv2___RNAseq.g21000.t1" 
## [20] "Pocillopora_acuta_HIv2___RNAseq.g20860.t1" 
## [21] "Pocillopora_acuta_HIv2___TS.g9414.t1"      
## [22] "Pocillopora_acuta_HIv2___RNAseq.g8588.t1"  
## [23] "Pocillopora_acuta_HIv2___RNAseq.g11588.t1" 
## [24] "Pocillopora_acuta_HIv2___RNAseq.g12281.t1" 
## [25] "Pocillopora_acuta_HIv2___TS.g27642.t1b"    
## [26] "Pocillopora_acuta_HIv2___RNAseq.g7803.t1"  
## [27] "Pocillopora_acuta_HIv2___RNAseq.g8119.t1"  
## [28] "Pocillopora_acuta_HIv2___RNAseq.g17117.t1" 
## [29] "Pocillopora_acuta_HIv2___RNAseq.g22978.t3b"
## [30] "Pocillopora_acuta_HIv2___RNAseq.30415_t"   
## [31] "Pocillopora_acuta_HIv2___TS.g16008.t2"     
## [32] "Pocillopora_acuta_HIv2___RNAseq.g2406.t1"  
## [33] "Pocillopora_acuta_HIv2___RNAseq.g25759.t1" 
## [34] "Pocillopora_acuta_HIv2___RNAseq.g24120.t1" 
## [35] "Pocillopora_acuta_HIv2___RNAseq.g14336.t1" 
## [36] "Pocillopora_acuta_HIv2___RNAseq.g9631.t1"  
## [37] "Pocillopora_acuta_HIv2___RNAseq.g7627.t1"  
## [38] "Pocillopora_acuta_HIv2___RNAseq.g8062.t1"  
## [39] "Pocillopora_acuta_HIv2___RNAseq.g1102.t1"  
## [40] "Pocillopora_acuta_HIv2___RNAseq.g14484.t1" 
## [41] "Pocillopora_acuta_HIv2___RNAseq.g21373.t1" 
## [42] "Pocillopora_acuta_HIv2___TS.g16384.t1"     
## [43] "Pocillopora_acuta_HIv2___RNAseq.g13561.t1" 
## [44] "Pocillopora_acuta_HIv2___RNAseq.g1126.t1"  
## [45] "Pocillopora_acuta_HIv2___RNAseq.g9210.t1"  
## [46] "Pocillopora_acuta_HIv2___RNAseq.g24649.t1" 
## [47] "Pocillopora_acuta_HIv2___RNAseq.g11659.t1" 
## [48] "Pocillopora_acuta_HIv2___TS.g25577.t1a"    
## [49] "Pocillopora_acuta_HIv2___RNAseq.g27681.t1b"
## [50] "Pocillopora_acuta_HIv2___TS.g1968.t2"
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "top50_LFC_DE_Manual")

#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), labels_col = NA, annotation_colors = ann_colors,
         labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 8.5)
up_Aboral
save_ggplot(up_Aboral, "up_Aboral_Manual")

#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), labels_col = NA, annotation_colors = ann_colors,
         labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 8.5)
up_OralEpi
save_ggplot(up_OralEpi, "up_OralEpi_Manual")

Expression of certain genes of interest: blast against P. acuta genome

yin yang Transctiption factor

cd ../references

#download the genome protein fasta if you have not already
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.pep.faa.gz

#unzip file
gunzip Pocillopora_acuta_HIv2.genes.pep.faa.gz

In unity

salloc -p cpu -c 8 --mem 32G

module load uri/main
module load BLAST+/2.15.0-gompi-2023a

#make blast_dbs directory if you haven't done so above
mkdir blast_dbs
cd blast_dbs

makeblastdb -in ../Pocillopora_acuta_HIv2.genes.pep.faa -out Pacuta_prot -dbtype prot

cd ../../output_RNA/differential_expression/

#make blast output directory if you haven't done so above
mkdir blast
cd blast

nano YinYang.txt

add accession numbers of interest:

  • nematostella vectensis transcriptional repressor protein YY1 isoforms
    • XP_048585772.1
    • XP_048585773.1
    • XP_048585774.1
  • human transcription factor YY2
    • NP_996806.2
  • human transcription factor ZFP41 (REX-1)
    • NP_777560.2
XP_048585772.1
XP_048585773.1
XP_048585774.1
NP_996806.2
NP_777560.2
# Read the input file line by line and fetch FASTA sequences
while read -r accession; do
  if [[ -n "$accession" ]]; then
    echo "Fetching $accession..."
    curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=protein&id=${accession}&rettype=fasta&retmode=text" >> "YinYang.fasta"
    echo >> "YinYang.fasta"  # Add a newline between sequences
    sleep 1  # Avoid hitting rate limits
  fi
done < "YinYang.txt"

# run blast with human-readable output
blastp -query YinYang.fasta -db ../../../references/blast_dbs/Pacuta_prot -out YinYang_blast_results.txt -outfmt 0

#looks like there are a lot of matches for each gene! I am going to do a tab search with a very low e-value cutoff:

# run blast with tabular output
blastp -query YinYang.fasta -db ../../../references/blast_dbs/Pacuta_prot -out YinYang_blast_results_tab.txt -outfmt 6 -evalue 1e-25

Great! Interestingly, the same gene, Pocillopora_acuta_HIv2_RNAseq.g25242.t1 is the top match for all 5 proteins I searched. Pocillopora_acuta_HIv2_TS.g24434.t1 is the second best match for all 5. That is interesting! Pocillopora_acuta_HIv2_RNAseq.g7583.t1 is also worth looking at. And Pocillopora_acuta_HIv2_TS.g21338.t1 matched all three YY1 isoforms.

YinYangs <- c("Pocillopora_acuta_HIv2___RNAseq.g25242.t1",
              "Pocillopora_acuta_HIv2___TS.g24434.t1",
              "Pocillopora_acuta_HIv2___RNAseq.g7583.t1",
              "Pocillopora_acuta_HIv2___TS.g21338.t1")

for (i in 1:length(YinYangs)){
  plotCounts(dds, gene=YinYangs[i], intgroup=c("Tissue"),)
}

as.data.frame(resOrdered)[YinYangs,]
##                                             baseMean log2FoldChange     lfcSE
## Pocillopora_acuta_HIv2___RNAseq.g25242.t1 2388.43282    -0.65267532 0.8125595
## Pocillopora_acuta_HIv2___TS.g24434.t1       86.14232    -0.16148954 1.0080043
## Pocillopora_acuta_HIv2___RNAseq.g7583.t1   429.08727     0.06121494 0.9629085
## Pocillopora_acuta_HIv2___TS.g21338.t1      277.12637    -0.03463576 1.0095974
##                                              pvalue      padj
## Pocillopora_acuta_HIv2___RNAseq.g25242.t1 0.1695296 0.3778825
## Pocillopora_acuta_HIv2___TS.g24434.t1     0.2933826 0.5402220
## Pocillopora_acuta_HIv2___RNAseq.g7583.t1  0.8212358 0.9266210
## Pocillopora_acuta_HIv2___TS.g21338.t1     0.2453329 0.4838418
##                                                                               query
## Pocillopora_acuta_HIv2___RNAseq.g25242.t1 Pocillopora_acuta_HIv2___RNAseq.g25242.t1
## Pocillopora_acuta_HIv2___TS.g24434.t1         Pocillopora_acuta_HIv2___TS.g24434.t1
## Pocillopora_acuta_HIv2___RNAseq.g7583.t1   Pocillopora_acuta_HIv2___RNAseq.g7583.t1
## Pocillopora_acuta_HIv2___TS.g21338.t1         Pocillopora_acuta_HIv2___TS.g21338.t1

Okay! None of these genes are differentially expressed between the tissues. That is interesting and good to know. Pocillopora_acuta_HIv2___RNAseq.g25242.t1 has the highest basal expression of all the potential isoforms of this transcription factor.

TRP Channels

DE_05_SwissProt$short_name <- ifelse(nchar(DE_05_SwissProt$ProteinNames) > 80, 
                            paste0(substr(DE_05_SwissProt$ProteinNames, 1, 77), "..."), 
                            DE_05_SwissProt$ProteinNames)

TRP <- Manual %>% filter(grepl("transient", ProteinNames, ignore.case = TRUE)) 
select <- TRP$query

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 12)
top50_DE
save_ggplot(top50_DE, "TRP_DE_SwissProt", width = 6.43, height = 4.25, units = "in", dpi = 300)

annot_tab$short_name <- ifelse(nchar(annot_tab$ProteinNames) > 80, 
                            paste0(substr(annot_tab$ProteinNames, 1, 77), "..."), 
                            annot_tab$ProteinNames)

swissprot_labels <- annot_tab  %>% 
  select(query,short_name) %>%
  mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes

TRP <- annot_tab %>% filter(grepl("transient", ProteinNames, ignore.case = TRUE))
select1 <- TRP$query
select <- match(select1,rownames(vsd))
select <- select[!is.na(select)]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = swissprot_labels[match(select1,swissprot_labels$query),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "TRP_SwissProt")

biomin de heatmap nice

DE_05_biomin_filtered <- DE_05_biomin %>% left_join(Manual,by="query" ) %>% filter(!(Heatmap_Label %in% c("Myosin-9", "Actin, cytoplasmic")))
  
select <- DE_05_biomin_filtered$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
DE_biomin <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
DE_biomin
save_ggplot(DE_biomin, "DE_biomin")

# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k as needed

# Create a dataframe to manage clusters and reordering
clustered_data <- data.frame(
  query = select,
  Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
  cluster = cluster_assignments
)

# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
  arrange(cluster, Heatmap_Label)

# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label

# Generate heatmap with reordered rows and labels
DE_biomin <- pheatmap(
  z_scores,
  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
  cluster_rows = FALSE,  # Disable clustering since rows are pre-ordered
  cluster_cols = TRUE,
  show_rownames = TRUE,
  cutree_cols = 2,
  annotation_col = (heatmap_metadata %>% select(Tissue)),
  annotation_colors = ann_colors,
  labels_row = ordered_labels,
  fontsize_row = 8
)
save_ggplot(DE_biomin, "clusters_clean/DE_biomin")

#############################

DE_05_Biomin_broc_filtered <- DE_05_Biomin_broc %>% left_join(Manual,by="query" ) %>% filter(!(Heatmap_Label %in% c("Myosin-9", "Actin, cytoplasmic")))
  
select <- DE_05_Biomin_broc_filtered$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
DE_Biomin_broc <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
DE_Biomin_broc
save_ggplot(DE_Biomin_broc, "DE_Biomin_broc")

# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k as needed

# Create a dataframe to manage clusters and reordering
clustered_data <- data.frame(
  query = select,
  Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
  cluster = cluster_assignments
)

# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
  arrange(cluster, Heatmap_Label)

# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label

# Generate heatmap with reordered rows and labels
DE_Biomin_broc <- pheatmap(
  z_scores,
  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
  cluster_rows = FALSE,  # Disable clustering since rows are pre-ordered
  cluster_cols = TRUE,
  show_rownames = TRUE,
  cutree_cols = 2,
  annotation_col = (heatmap_metadata %>% select(Tissue)),
  annotation_colors = ann_colors,
  labels_row = ordered_labels,
  fontsize_row = 8
)
save_ggplot(DE_Biomin_broc, "clusters_clean/DE_Biomin_broc")

###wnt and hox

WNT <- Manual %>% filter(grepl("wnt", Heatmap_Label, ignore.case = TRUE)|grepl("frizzle", Heatmap_Label, ignore.case = TRUE)|grepl("homeobox", Heatmap_Label, ignore.case = TRUE)|grepl("hox", Heatmap_Label, ignore.case = TRUE)|grepl("forkhead", Heatmap_Label, ignore.case = TRUE))#|grepl("wnt", BiologicalProcess, ignore.case = TRUE))
select <- WNT$query

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "WNT_Hox_DE_SwissProt")

# Perform clustering
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters

# Create a dataframe for clustering and label management
clustered_data <- data.frame(
  query = select,
  Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
  cluster = cluster_assignments
)

# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
  arrange(cluster, Heatmap_Label)

# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label

# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1])  # Only where cluster switches

# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
  z_scores,
  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
  cluster_rows = FALSE,  # Disable clustering since rows are pre-ordered
  cluster_cols = TRUE,
  cutree_cols = 2,
  annotation_col = (heatmap_metadata %>% select(Tissue)),
  annotation_colors = ann_colors,
  labels_row = ordered_labels,
  fontsize_row = 8,
  borders_color = "white",  # Optional border around clusters
  cutree_rows = 2,  # Keep row clustering if desired
  gaps_row = 24  # Add breaks only between clusters
)


# Save the heatmap
save_ggplot(top50_DE, "clusters_clean/WNT_Hox_DE_SwissProt")

wnt chromosome

WNT <- Manual %>% filter(grepl("wnt", Heatmap_Label, ignore.case = TRUE))
select <- WNT$query

gene_layout <- gff_transcripts %>%
  filter(query %in% select) %>%
  arrange(chromosome, start) %>% left_join(WNT) %>% distinct() 

expr_df <- assay(vsd)[select, ] %>%
  as.data.frame() %>%
  rownames_to_column("query") %>%
  pivot_longer(-query, names_to = "Sample", values_to = "vsd_expr") %>%
  left_join(as.data.frame(colData(vsd)), by = "Sample") %>%
  group_by(query, Tissue) %>%
  summarise(mean_expr = mean(vsd_expr), .groups = "drop") %>%
  pivot_wider(names_from = Tissue, values_from = mean_expr)

plot_df <- gene_layout %>%
  left_join(expr_df, by = "query")

plot_df <- plot_df %>%
  mutate(highest_expr_tissue = case_when(
    OralEpi > Aboral ~ "OralEpi",
    Aboral > OralEpi ~ "Aboral",
    TRUE ~ "equal"
  ))

plot_df <- plot_df %>%
  mutate(chromosome_split = case_when(
    chromosome == "Pocillopora_acuta_HIv2___xfSc0000014" & start < 3.6e6 ~ "xfSc0000014_A",
    chromosome == "Pocillopora_acuta_HIv2___xfSc0000014" & start >= 3.65e6 ~ "xfSc0000014_B",
    TRUE ~ chromosome
  ))

plot_df$Heatmap_Label <- gsub("Protein ","",plot_df$Heatmap_Label)

heat_df <- plot_df %>%
  select(query, start, end, width,chromosome, OralEpi, Aboral) %>%
  pivot_longer(cols = c(OralEpi, Aboral), names_to = "tissue", values_to = "expression") %>%
  mutate(midpoint = (start + end) / 2)  # for positioning tile center

heat_df <- heat_df %>%
  left_join(plot_df %>% select(query, chromosome_split), by = "query") %>%
  mutate(chromosome = chromosome_split)

library(gggenes)
library(ggnewscale)

Wnt_chr <- ggplot(plot_df, aes(xmin = start, xmax = end, y = "Hox genes", forward = strand == "+",
                    fill = highest_expr_tissue)) +
  geom_gene_arrow(arrowhead_height = unit(3, "mm"), arrowhead_width = unit(1.5, "mm")) +
  geom_label(data = plot_df,
           aes(x = (start + end)/2, y = "_Genes_ ", label = Heatmap_Label),
           vjust = -1.75, size = 2, fill = "lightgrey", label.size = 0.1,fontface="bold")+
  scale_fill_manual(values = c("OralEpi" = "palegreen3", "Aboral" = "mediumpurple1", "equal" = "grey")) +
  theme_minimal() +
  facet_wrap(~ chromosome_split, scales = "free_x")+
  theme(axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        panel.spacing = unit(2, "lines")) +
  labs(x = "Genomic position", fill = "Higher expression in")
Wnt_chr

save_ggplot(Wnt_chr, "Wnt_chromosome")

Wnt_chr_heatmap <- ggplot() +
  # Heatmap tiles
  geom_tile(data = heat_df,
            aes(x = midpoint, y = tissue, fill = expression,width = width),colour="black",
            height = 0.5) +
  
  # Color for heatmap (expression level)
  scale_fill_gradient(low = "white", high = "firebrick", name = "Expression") +
  new_scale_fill() +
  
  # Gene arrows
  geom_gene_arrow(data = plot_df,
                  aes(xmin = start, xmax = end, y = "_Genes_",
                      forward = strand == "+", fill = highest_expr_tissue),
                  arrowhead_height = unit(3, "mm"),
                  arrowhead_width = unit(1.5, "mm")) +
    geom_label(data = plot_df,
           aes(x = (start + end)/2, y = "_Genes_", label = Heatmap_Label),
           vjust = 1.5, size = 4, fill = "lightgrey", label.size = 0.1,fontface="bold")+

  scale_fill_manual(values = c("OralEpi" = "palegreen3", "Aboral" = "mediumpurple1", "equal" = "grey"),
                    name = "Higher expression in") +

  facet_wrap(~ chromosome_split, scales = "free_x") +
  theme_minimal() +
  theme(
    axis.text.y = element_text(size = 10),
    axis.title.y = element_blank(),
    panel.spacing = unit(.1, "lines")
  ) +scale_y_discrete(expand = expansion(mult = c(1, 0)))+
  labs(x = "Genomic position")

save_ggplot(Wnt_chr_heatmap, "Wnt_chromosome_expression",width = 20, height = 5)

# Shift gene coordinates within each chromosome
plot_df_shifted <- plot_df %>%
  group_by(chromosome) %>%
  mutate(
    chr_start = min(start),
    start_shifted = start - chr_start + 10,  # +10 if you want to avoid 0
    end_shifted   = end - chr_start + 10
  ) %>%
  ungroup()


heat_df_shifted <- plot_df_shifted %>%
  select(query, start_shifted, end_shifted, width,chromosome, OralEpi, Aboral) %>%
  pivot_longer(cols = c(OralEpi, Aboral), names_to = "tissue", values_to = "expression") %>%
  mutate(midpoint = (start_shifted + end_shifted) / 2)  # for positioning tile center

Wnt_chr_heatmap <- ggplot() +
  # Heatmap tiles
  geom_tile(data = heat_df_shifted,
            aes(x = midpoint, y = tissue, fill = expression,width = width),colour="black",
            height = 0.5) +
  
  # Color for heatmap (expression level)
  scale_fill_gradient(low = "white", high = "firebrick", name = "Expression") +
  new_scale_fill() +
  
  # Gene arrows
  geom_gene_arrow(data = plot_df_shifted,
                  aes(xmin = start_shifted, xmax = end_shifted, y = "_Genes_",
                      forward = strand == "+", fill = highest_expr_tissue),
                  arrowhead_height = unit(3, "mm"),
                  arrowhead_width = unit(1.5, "mm")) +
    geom_label(data = plot_df_shifted,
           aes(x = (start_shifted + end_shifted)/2, y = "_Genes_", label = Heatmap_Label),
           vjust = 1.5, size = 4, fill = "lightgrey", label.size = 0.1,fontface="bold")+

  scale_fill_manual(values = c("OralEpi" = "palegreen3", "Aboral" = "mediumpurple1", "equal" = "grey"),
                    name = "Higher expression in") +

  facet_wrap(~ chromosome, scales = "fixed",ncol=1) +
  theme_minimal() +
  theme(
    axis.text.y = element_text(size = 10),
    axis.title.y = element_blank(),
    panel.spacing = unit(.1, "lines")
  ) +scale_y_discrete(expand = expansion(mult = c(1, 0)))+
  labs(x = "Genomic position")

save_ggplot(Wnt_chr_heatmap, "Wnt_chromosome_expression_length_preserved",width = 30, height = 5)

Hox

WNT <- Manual %>% filter(grepl("homeobox", Heatmap_Label, ignore.case = TRUE)|grepl("hox", Heatmap_Label, ignore.case = TRUE)|grepl("forkhead", Heatmap_Label, ignore.case = TRUE))
select <- WNT$query

gene_layout <- gff_transcripts %>%
  filter(query %in% select) %>%
  arrange(chromosome, start) %>% left_join(WNT) %>% distinct() 

expr_df <- assay(vsd)[select, ] %>%
  as.data.frame() %>%
  rownames_to_column("query") %>%
  pivot_longer(-query, names_to = "Sample", values_to = "vsd_expr") %>%
  left_join(as.data.frame(colData(vsd)), by = "Sample") %>%
  group_by(query, Tissue) %>%
  summarise(mean_expr = mean(vsd_expr), .groups = "drop") %>%
  pivot_wider(names_from = Tissue, values_from = mean_expr)

plot_df <- gene_layout %>%
  left_join(expr_df, by = "query")

plot_df <- plot_df %>%
  mutate(highest_expr_tissue = case_when(
    OralEpi > Aboral ~ "OralEpi",
    Aboral > OralEpi ~ "Aboral",
    TRUE ~ "equal"
  ))

plot_df <- plot_df %>%
  mutate(chromosome_split = case_when(
    chromosome == "Pocillopora_acuta_HIv2___xfSc0000002" & start < 2.2e6 ~ "xfSc0000002_A",
    chromosome == "Pocillopora_acuta_HIv2___xfSc0000002" & start >= 6.4e6 ~ "xfSc0000002_B",
      chromosome == "Pocillopora_acuta_HIv2___Sc0000011" & start < 2e6 ~ "Sc0000011_A",
      chromosome == "Pocillopora_acuta_HIv2___Sc0000011" & start >= 2e6 ~ "Sc0000011_B",
    chromosome == "Pocillopora_acuta_HIv2___Sc0000024" & start < 420000 ~ "Sc0000024_A",
    chromosome == "Pocillopora_acuta_HIv2___Sc0000024" & start >= 2300000 ~ "Sc0000024_B",
          chromosome == "Pocillopora_acuta_HIv2___xfSc0000008" & start < 2e6 ~ "xfSc0000008_A",
      chromosome == "Pocillopora_acuta_HIv2___xfSc0000008" & start >= 2e6 ~ "xfSc0000008_B",
    TRUE ~ chromosome
  ))

heat_df <- plot_df %>%
  select(query, start, end, width,chromosome, OralEpi, Aboral) %>%
  pivot_longer(cols = c(OralEpi, Aboral), names_to = "tissue", values_to = "expression") %>%
  mutate(midpoint = (start + end) / 2)  # for positioning tile center

heat_df <- heat_df %>%
  left_join(plot_df %>% select(query, chromosome_split), by = "query") %>%
  mutate(chromosome = chromosome_split)

library(gggenes)
library(ggnewscale)

Wnt_chr <- ggplot(plot_df[1:6,], aes(xmin = start, xmax = end, y = "Hox genes", forward = strand == "+",
                    fill = highest_expr_tissue)) +
  geom_gene_arrow(arrowhead_height = unit(3, "mm"), arrowhead_width = unit(1.5, "mm")) +
  geom_label(aes(x = (start + end)/2, y = "_Genes_ ", label = Heatmap_Label),
           vjust = -1.75, size = 2, fill = "lightgrey", label.size = 0.1,fontface="bold")+
  scale_fill_manual(values = c("OralEpi" = "palegreen3", "Aboral" = "mediumpurple1", "equal" = "grey")) +
  theme_minimal() +
  facet_wrap(~ chromosome_split, scales = "free_x",ncol=1)+
  theme(axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        panel.spacing = unit(2, "lines")) +
  labs(x = "Genomic position", fill = "Higher expression in")
Wnt_chr

 save_ggplot(Wnt_chr, "Hox_chromosome")

 Wnt_chr <- ggplot(plot_df[7:12,], aes(xmin = start, xmax = end, y = "Hox genes", forward = strand == "+",
                    fill = highest_expr_tissue)) +
  geom_gene_arrow(arrowhead_height = unit(3, "mm"), arrowhead_width = unit(1.5, "mm")) +
  geom_label(aes(x = (start + end)/2, y = "_Genes_ ", label = Heatmap_Label),
           vjust = -1.75, size = 2, fill = "lightgrey", label.size = 0.1,fontface="bold")+
  scale_fill_manual(values = c("OralEpi" = "palegreen3", "Aboral" = "mediumpurple1", "equal" = "grey")) +
  theme_minimal() +
  facet_wrap(~ chromosome_split, scales = "free_x",ncol=1)+
  theme(axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        panel.spacing = unit(2, "lines")) +
  labs(x = "Genomic position", fill = "Higher expression in")
Wnt_chr

 save_ggplot(Wnt_chr, "Hox_chromosome2")

  Wnt_chr <- ggplot(plot_df[13:19,], aes(xmin = start, xmax = end, y = "Hox genes", forward = strand == "+",
                    fill = highest_expr_tissue)) +
  geom_gene_arrow(arrowhead_height = unit(3, "mm"), arrowhead_width = unit(1.5, "mm")) +
  geom_label(aes(x = (start + end)/2, y = "_Genes_ ", label = Heatmap_Label),
           vjust = -1.75, size = 2, fill = "lightgrey", label.size = 0.1,fontface="bold")+
  scale_fill_manual(values = c("OralEpi" = "palegreen3", "Aboral" = "mediumpurple1", "equal" = "grey")) +
  theme_minimal() +
  facet_wrap(~ chromosome_split, scales = "free_x",ncol=1)+
  theme(axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        panel.spacing = unit(2, "lines")) +
  labs(x = "Genomic position", fill = "Higher expression in")
Wnt_chr

 save_ggplot(Wnt_chr, "Hox_chromosome3")

   Wnt_chr <- ggplot(plot_df[20:24,], aes(xmin = start, xmax = end, y = "Hox genes", forward = strand == "+",
                    fill = highest_expr_tissue)) +
  geom_gene_arrow(arrowhead_height = unit(3, "mm"), arrowhead_width = unit(1.5, "mm")) +
  geom_label(aes(x = (start + end)/2, y = "_Genes_ ", label = Heatmap_Label),
           vjust = -1.75, size = 2, fill = "lightgrey", label.size = 0.1,fontface="bold")+
  scale_fill_manual(values = c("OralEpi" = "palegreen3", "Aboral" = "mediumpurple1", "equal" = "grey")) +
  theme_minimal() +
  facet_wrap(~ chromosome_split, scales = "free_x",ncol=1)+
  theme(axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        panel.spacing = unit(2, "lines")) +
  labs(x = "Genomic position", fill = "Higher expression in")
Wnt_chr

 save_ggplot(Wnt_chr, "Hox_chromosome4")

Wnt_chr_heatmap <- ggplot() +
  # Heatmap tiles
  geom_tile(data = heat_df,
            aes(x = midpoint, y = tissue, fill = expression,width = width),colour="black",
            height = 0.5) +
  
  # Color for heatmap (expression level)
  scale_fill_gradient(low = "white", high = "firebrick", name = "Expression") +
  new_scale_fill() +
  
  # Gene arrows
  geom_gene_arrow(data = plot_df,
                  aes(xmin = start, xmax = end, y = "_Genes_",
                      forward = strand == "+", fill = highest_expr_tissue),
                  arrowhead_height = unit(3, "mm"),
                  arrowhead_width = unit(1.5, "mm")) +
    geom_label(data = plot_df,
           aes(x = (start + end)/2, y = "_Genes_", label = Heatmap_Label),
           vjust = 1.5, size = 2 , fill = "lightgrey", label.size = 0.1,fontface="bold")+

  scale_fill_manual(values = c("OralEpi" = "palegreen3", "Aboral" = "mediumpurple1", "equal" = "grey"),
                    name = "Higher expression in") +

  facet_wrap(~ chromosome_split, scales = "free_x") +
  theme_minimal() +
  theme(
    axis.text.y = element_text(size = 5),
    axis.title.y = element_blank(),
    panel.spacing = unit(.1, "lines")
  ) +#scale_y_discrete(expand = expansion(mult = c(1, 0)))+
  labs(x = "Genomic position")

save_ggplot(Wnt_chr_heatmap, "Hox_chromosome_expression",width = 20, height = 5)

# Shift gene coordinates within each chromosome
plot_df_shifted <- plot_df %>%
  group_by(chromosome_split) %>%
  mutate(
    chr_start = min(start),
    start_shifted = start - chr_start + 10,  # +10 if you want to avoid 0
    end_shifted   = end - chr_start + 10
  ) %>%
  ungroup()


heat_df_shifted <- plot_df_shifted %>%
  select(query, start_shifted, end_shifted, width,chromosome_split, OralEpi, Aboral) %>%
  pivot_longer(cols = c(OralEpi, Aboral), names_to = "tissue", values_to = "expression") %>%
  mutate(midpoint = (start_shifted + end_shifted) / 2)  # for positioning tile center

Wnt_chr_heatmap <- ggplot() +
  # Heatmap tiles
  geom_tile(data = heat_df_shifted,
            aes(x = midpoint, y = tissue, fill = expression,width = width),colour="black",
            height = 1) +
  
  # Color for heatmap (expression level)
  scale_fill_gradient(low = "white", high = "firebrick", name = "Expression") +
  new_scale_fill() +
  
  # Gene arrows
  geom_gene_arrow(data = plot_df_shifted,
                  aes(xmin = start_shifted, xmax = end_shifted, y = "_Genes_",
                      forward = strand == "+", fill = highest_expr_tissue),
                  arrowhead_height = unit(3, "mm"),
                  arrowhead_width = unit(1.5, "mm")) +
    geom_label(data = plot_df_shifted,
           aes(x = (start_shifted + end_shifted)/2, y = "_Genes_", label = Heatmap_Label),
           vjust = 1.5, size = 4, fill = "lightgrey", label.size = 0.1,fontface="bold")+

  scale_fill_manual(values = c("OralEpi" = "palegreen3", "Aboral" = "mediumpurple1", "equal" = "grey"),
                    name = "Higher expression in") +

  facet_wrap(~ chromosome_split, scales = "fixed",ncol=1) +
  theme_minimal() +
  theme(
    axis.text.y = element_text(size = 10),
    axis.title.y = element_blank(),
    panel.spacing = unit(.1, "lines")
  ) +scale_y_discrete(expand = expansion(mult = c(1, 0)))+
  labs(x = "Genomic position")

save_ggplot(Wnt_chr_heatmap, "Hox_chromosome_expression_length_preserved",width = 25, height = 20)

Hox genes Nematostella

differentially expressed

select <- DE_05_Hox$query

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "Hox_Nvec_DE_SwissProt")

# Perform clustering
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters

# Create a dataframe for clustering and label management
clustered_data <- data.frame(
  query = select,
  Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
  cluster = cluster_assignments
)

# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
  arrange(cluster, Heatmap_Label)

# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label

# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1])  # Only where cluster switches

# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
  z_scores,
  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
  cluster_rows = FALSE,  # Disable clustering since rows are pre-ordered
  cluster_cols = TRUE,
  cutree_cols = 2,
  annotation_col = (heatmap_metadata %>% select(Tissue)),
  annotation_colors = ann_colors,
  labels_row = ordered_labels,
  fontsize_row = 8,
  borders_color = "white",  # Optional border around clusters
  cutree_rows = 2,  # Keep row clustering if desired
  gaps_row = 4  # Add breaks only between clusters
)


# Save the heatmap
save_ggplot(top50_DE, "clusters_clean/Hox_Nvec_DE_SwissProt")

all

labels <- DESeq_Hox %>% drop_na() %>% pull(def_short)

select <- DESeq_Hox %>% drop_na() %>% pull(query)

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = labels, fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "Hox_Nvec_all_SwissProt")

# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters

# Create a dataframe for clustering and label management
clustered_data <- data.frame(
  query = select,
  Heatmap_Label = labels,
  cluster = cluster_assignments
)

# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
  arrange(cluster, Heatmap_Label)

# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label

# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1])  # Only where cluster switches

# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
  z_scores,
  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
  cluster_rows = FALSE,  # Disable clustering since rows are pre-ordered
  cluster_cols = TRUE,
  cutree_cols = 2,
  annotation_col = (heatmap_metadata %>% select(Tissue)),
  annotation_colors = ann_colors,
  labels_row = ordered_labels,
  fontsize_row = 8,
  borders_color = "white",  # Optional border around clusters
  cutree_rows = 2,  # Keep row clustering if desired
  gaps_row = 9  # Add breaks only between clusters
)


# Save the heatmap
save_ggplot(top50_DE, "clusters_clean/Hox_Nvec_all_SwissProt")

gene_layout <- gff_transcripts %>%
  filter(query %in% select) %>%
  arrange(chromosome, start) %>% left_join(DESeq_Hox) %>% distinct() 

expr_df <- assay(vsd)[select, ] %>%
  as.data.frame() %>%
  rownames_to_column("query") %>%
  pivot_longer(-query, names_to = "Sample", values_to = "vsd_expr") %>%
  left_join(as.data.frame(colData(vsd)), by = "Sample") %>%
  group_by(query, Tissue) %>%
  summarise(mean_expr = mean(vsd_expr), .groups = "drop") %>%
  pivot_wider(names_from = Tissue, values_from = mean_expr)

plot_df <- gene_layout %>%
  left_join(expr_df, by = "query")

plot_df <- plot_df %>%
  mutate(highest_expr_tissue = case_when(
    OralEpi > Aboral ~ "OralEpi",
    Aboral > OralEpi ~ "Aboral",
    TRUE ~ "equal"
  ))

heat_df <- plot_df %>%
  select(query, start, end, width,chromosome, OralEpi, Aboral) %>%
  pivot_longer(cols = c(OralEpi, Aboral), names_to = "tissue", values_to = "expression") %>%
  mutate(midpoint = (start + end) / 2)  # for positioning tile center


library(gggenes)
library(ggnewscale)

Hox_chr <- ggplot(plot_df, aes(xmin = start, xmax = end, y = "Hox genes", forward = strand == "+",
                    fill = highest_expr_tissue)) +
  geom_gene_arrow(arrowhead_height = unit(3, "mm"), arrowhead_width = unit(1.5, "mm")) +
  geom_gene_label(aes(label = Gene_Name), size = 50) +
  scale_fill_manual(values = c("OralEpi" = "palegreen3", "Aboral" = "mediumpurple1", "equal" = "grey")) +
  theme_minimal() +
  facet_wrap(~ chromosome, scales = "free_x", ncol = 1)+
  theme(axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        panel.spacing = unit(.1, "lines")) +
  labs(x = "Genomic position", fill = "Higher expression in")


Hox_chr_heatmap <- ggplot() +
  # Heatmap tiles
  geom_tile(data = heat_df,
            aes(x = midpoint, y = tissue, fill = expression),colour="black",
            height = 0.4) +
  
  # Color for heatmap (expression level)
  scale_fill_gradient(low = "white", high = "firebrick", name = "Expression") +
  new_scale_fill() +
  
  # Gene arrows
  geom_gene_arrow(data = plot_df,
                  aes(xmin = start, xmax = end, y = "_Genes_",
                      forward = strand == "+", fill = highest_expr_tissue),
                  arrowhead_height = unit(3, "mm"),
                  arrowhead_width = unit(1.5, "mm")) +

  geom_gene_label(data = plot_df,
                  aes(xmin = start, xmax = end, y = "_Genes_", label = Gene_Name),
                  size = 50) +

  scale_fill_manual(values = c("OralEpi" = "palegreen3", "Aboral" = "mediumpurple1", "equal" = "grey"),
                    name = "Higher expression in") +
  facet_wrap(~ chromosome, scales = "free_x", ncol = 1) +
  theme_minimal() +
  theme(
    axis.text.y = element_text(size = 10),
    axis.title.y = element_blank(),
    panel.spacing = unit(.1, "lines")
  ) +
  labs(x = "Genomic position")

save_ggplot(Hox_chr, "Hox_Nvec_all_SwissProt_chromosome")

save_ggplot(Hox_chr_heatmap, "Hox_Nvec_all_SwissProt_chromosome_expression")

He et al 2023 TFs/genes of interest Nematostella

differentially expressed

select <- unique(DE_05_He_etal$query)

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "He_etal_Nvec_DE_SwissProt")

# Perform clustering
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters

# Create a dataframe for clustering and label management
clustered_data <- data.frame(
  query = select,
  Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
  cluster = cluster_assignments
)

# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
  arrange(cluster, Heatmap_Label)

# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label

# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1])  # Only where cluster switches

# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
  z_scores,
  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
  cluster_rows = FALSE,  # Disable clustering since rows are pre-ordered
  cluster_cols = TRUE,
  cutree_cols = 2,
  annotation_col = (heatmap_metadata %>% select(Tissue)),
  annotation_colors = ann_colors,
  labels_row = ordered_labels,
  fontsize_row = 8,
  borders_color = "white",  # Optional border around clusters
  cutree_rows = 2,  # Keep row clustering if desired
  gaps_row = 4  # Add breaks only between clusters
)

# Save the heatmap
save_ggplot(top50_DE, "clusters_clean/He_etal_Nvec_DE_SwissProt")

### DuBuc et al 2018 Wnt/Aboral-Oral Patterning Genes, interest Nematostella

differentially expressed

select <- unique(DE_05_DuBuc_etal$query)

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "DuBuc_etal_Nvec_DE_SwissProt")

# Perform clustering
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters

# Create a dataframe for clustering and label management
clustered_data <- data.frame(
  query = select,
  Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
  cluster = cluster_assignments
)

# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
  arrange(cluster, Heatmap_Label)

# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label

# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1])  # Only where cluster switches

# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
  z_scores,
  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
  cluster_rows = FALSE,  # Disable clustering since rows are pre-ordered
  cluster_cols = TRUE,
  cutree_cols = 2,
  annotation_col = (heatmap_metadata %>% select(Tissue)),
  annotation_colors = ann_colors,
  labels_row = ordered_labels,
  fontsize_row = 8,
  borders_color = "white"#,  # Optional border around clusters
  #cutree_rows = 2,  # Keep row clustering if desired
  #gaps_row = 4  # Add breaks only between clusters
)

# Save the heatmap
save_ggplot(top50_DE, "clusters_clean/DuBuc_etal_Nvec_DE_SwissProt")

bacteria genes

mucin <- Manual %>% filter(grepl("mucin", ProteinNames, ignore.case = TRUE)|grepl("toll-", ProteinNames, ignore.case = TRUE)|grepl("ZP", ProteinNames, ignore.case = TRUE)|grepl("lectin", ProteinNames, ignore.case = TRUE)|grepl("nitric", Heatmap_Label, ignore.case = TRUE)) %>% filter(Heatmap_Label !="Cnidocyte marker protein (Collectin-11)")
select <- mucin$query

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "bacteria_DE_SwissProt")

# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters

# Create a dataframe for clustering and label management
clustered_data <- data.frame(
  query = select,
  Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
  cluster = cluster_assignments
)

# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
  arrange(cluster, Heatmap_Label)

# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label

# Generate the heatmap with reordered rows and labels
mucins <- pheatmap(
  z_scores,
  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
  cluster_rows = FALSE,  # Disable clustering since rows are pre-ordered
  cluster_cols = TRUE,
  cutree_cols = 2,
  annotation_col = (heatmap_metadata %>% select(Tissue)),
  annotation_colors = ann_colors,
  labels_row = ordered_labels,
  fontsize_row = 8,
  borders_color = "white",  # Optional border around clusters
  cutree_rows = 2,  # Keep row clustering if desired
  gaps_row = c(24,38)  # Add breaks only between clusters
)

# Save the heatmap
save_ggplot(mucins, "clusters_clean/bacteria_DE_SwissProt")

temp and light sensing

sensors <- Manual %>% filter(grepl("TRP", Heatmap_Label, ignore.case = TRUE)|grepl("cellular response to light stimulus", BiologicalProcess, ignore.case = TRUE)|grepl("detection of mechanical stimulus involved in sensory perception", BiologicalProcess, ignore.case = TRUE))


select <- sensors$query

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_colors = ann_colors,
         labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 3)
top50_DE
save_ggplot(top50_DE, "sensors_DE_SwissProt")

# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 3) # Adjust k for the number of clusters

# Create a dataframe for clustering and label management
clustered_data <- data.frame(
  query = select,
  Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
  cluster = cluster_assignments
)

# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
  mutate(cluster = factor(cluster, levels = c(1, 3, 2))) %>%
  arrange(cluster, Heatmap_Label)

# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label

# Generate the heatmap with reordered rows and labels
transporters_heat <- pheatmap(
  z_scores,
  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
  cluster_rows = FALSE,  # Disable clustering since rows are pre-ordered
  cluster_cols = TRUE,
  cutree_cols = 2,
  annotation_col = (heatmap_metadata %>% select(Tissue)),
  annotation_colors = ann_colors,
  labels_row = ordered_labels,
  fontsize_row = 8,
  borders_color = "white",  # Optional border around clusters
  cutree_rows = 2,  # Keep row clustering if desired
  gaps_row = c(10,18)  # Add breaks only between clusters
)

# Save the heatmap
save_ggplot(transporters_heat, "clusters_clean/sensors_DE_SwissProt")

Heat Stress Genes

differentially expressed

select <- DE_05_HeatStressGenes %>% arrange(response_type) %>% select(query,gene_name,response_type) %>% unique()
rownames(select) <- select$query

z_scores <- t(scale(t(assay(vsd)[select$query, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_row = (select %>% select(response_type)), annotation_colors = ann_colors,
         labels_row = select$gene_name, fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "HeatStress_DE_SwissProt")

# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters

# Create a dataframe for clustering and label management
clustered_data <- data.frame(
  query = select$query,
  Heatmap_Label = select$gene_name,
  cluster = cluster_assignments
)

# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
  arrange(cluster, Heatmap_Label)

# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label

# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1])  # Only where cluster switches

# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
  z_scores,
  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
  cluster_rows = FALSE,  # Disable clustering since rows are pre-ordered
  cluster_cols = TRUE,
  cutree_cols = 2,
  annotation_col = (heatmap_metadata %>% select(Tissue)),
  annotation_colors = ann_colors,
  labels_row = ordered_labels,
  annotation_row = (select %>% select(response_type)),
  fontsize_row = 8,
  borders_color = "white",  # Optional border around clusters
  cutree_rows = 2,  # Keep row clustering if desired
  gaps_row = 4  # Add breaks only between clusters
)


# Save the heatmap
save_ggplot(top50_DE, "clusters_clean/HeatStress_DE_SwissProt")

all

select <- DESeq_HeatStressGenes %>% arrange(response_type) %>% select(query,gene_name,response_type) %>%
    group_by(query) %>% #collapse duplicate rows
  summarise(
    gene_name = paste(unique(gene_name), collapse = "; "),
    response_type = paste(unique(response_type), collapse = "; "),
    .groups = "drop"
  ) %>% arrange(response_type,gene_name)
select <- data.frame(select)
rownames(select) <- select$query

z_scores <- t(scale(t(assay(vsd)[select$query, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=(heatmap_metadata%>% select(Tissue)), annotation_row = (select %>% select(response_type)), annotation_colors = ann_colors,
         labels_row = select$gene_name, fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "HeatStress_all_SwissProt")

# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters

# Create a dataframe for clustering and label management
clustered_data <- data.frame(
  query = select$query,
  Heatmap_Label = select$gene_name,
  cluster = cluster_assignments
)

# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
  arrange(cluster, Heatmap_Label)

# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label

# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1])  # Only where cluster switches

# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
  z_scores,
  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
  cluster_rows = FALSE,  # Disable clustering since rows are pre-ordered
  cluster_cols = TRUE,
  cutree_cols = 2,
  annotation_col = (heatmap_metadata %>% select(Tissue)),
  annotation_colors = ann_colors,
  labels_row = ordered_labels,
  annotation_row = (select %>% select(response_type)),
  fontsize_row = 8,
  borders_color = "white",  # Optional border around clusters
  cutree_rows = 2,  # Keep row clustering if desired
  gaps_row = 4  # Add breaks only between clusters
)

# Save the heatmap
save_ggplot(top50_DE, "clusters_clean/HeatStress_all_SwissProt")

random

# Function to generate short names for proteins
generate_short_name <- function(data) {
  data %>%
    mutate(short_name = ifelse(nchar(ProteinNames) > 60, 
                               paste0(substr(ProteinNames, 1, 57), "..."), 
                               ProteinNames))
}

# Function to create gene labels
create_gene_labels <- function(data) {
  data %>%
    select(query, short_name) %>%
    mutate_all(~ ifelse(is.na(.), "", .)) # Replace NAs with ""
}

# Function to generate z-scores for selected genes
calculate_z_scores <- function(data, selection, vsd_matrix) {
  selected_genes <- match(selection, rownames(vsd_matrix))
  selected_genes <- selected_genes[!is.na(selected_genes)] # Remove NAs
  t(scale(t(assay(vsd_matrix)[selected_genes, ]), center = TRUE, scale = TRUE))
}

# Function to create and save heatmap
create_heatmap <- function(z_scores, labels_row, annotation_col, annotation_colors, filename) {
  heatmap <- pheatmap(z_scores, 
                      color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
                      cluster_rows = TRUE, 
                      show_rownames = TRUE,
                      cluster_cols = TRUE, 
                      cutree_cols = 2,
                      annotation_col = annotation_col,
                      annotation_colors = annotation_colors,
                      labels_row = labels_row, 
                      fontsize_row = 6)
  save_ggplot(heatmap, filename)
}

# Process DE_05_SwissProt
DE_05_SwissProt <- generate_short_name(DE_05_SwissProt)
gene_labels <- create_gene_labels(DE_05_SwissProt)
TRP <- DE_05_SwissProt %>% filter(grepl("transient", ProteinNames, ignore.case = TRUE))
select <- TRP$query
z_scores <- calculate_z_scores(DE_05_SwissProt, select, vsd)
create_heatmap(z_scores, 
               labels_row = gene_labels[match(select, gene_labels$query), 2], 
               annotation_col = heatmap_metadata %>% select(Tissue), 
               annotation_colors = ann_colors, 
               filename = "TRP_DE_SwissProt")

# Process annot_tab
annot_tab <- generate_short_name(annot_tab)
gene_labels <- create_gene_labels(annot_tab)
TRP <- annot_tab %>% filter(grepl("transient", ProteinNames, ignore.case = TRUE))
TRP <- left_join(TRP, as.data.frame(resOrdered)) %>% filter(!is.na(log2FoldChange))
select1 <- TRP$query
z_scores <- calculate_z_scores(annot_tab, select1, vsd)
create_heatmap(z_scores, 
               labels_row = TRP$short_name, 
               annotation_col = heatmap_metadata %>% select(Tissue), 
               annotation_colors = ann_colors, 
               filename = "TRP_SwissProt")

significant <- ifelse(row.names(z_scores) %in% DE_05$query, "Significant", "Not Significant")

# Add this as a new annotation
row_annotation <- data.frame(Significance = significant)
rownames(row_annotation) <- rownames(z_scores)
row_annotation_colors <- list(Significance = c("Significant" = "red", "Not Significant" = "grey"))

heatmap <- pheatmap(z_scores,  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
                      cluster_rows = TRUE, 
                      show_rownames = TRUE,
                      cluster_cols = TRUE, 
                      cutree_cols = 2,
                    cutree_rows = 5,
                      annotation_col = heatmap_metadata %>% select(Tissue),
                      annotation_colors = ann_colors,
                    annotation_row = row_annotation,
          annotation_row_colors = row_annotation_colors,
                      labels_row = TRP$short_name, 
                      fontsize_row = 12)
  save_ggplot(heatmap, "TRP_SwissProt")

# Process DE_05_SwissProt
DE_05_SwissProt <- generate_short_name(DE_05_SwissProt)
gene_labels <- create_gene_labels(DE_05_SwissProt)
GFP <- DE_05_SwissProt %>% filter(grepl("biolum", BiologicalProcess, ignore.case = TRUE))
select <- GFP$query
z_scores <- calculate_z_scores(DE_05_SwissProt, select, vsd)
create_heatmap(z_scores, 
               labels_row = gene_labels[match(select, gene_labels$query), 2], 
               annotation_col = heatmap_metadata %>% select(Tissue), 
               annotation_colors = ann_colors, 
               filename = "GFP_DE_SwissProt")

# Process annot_tab
annot_tab <- generate_short_name(annot_tab)
gene_labels <- create_gene_labels(annot_tab)
GFP <- annot_tab %>% filter(grepl("biolum", BiologicalProcess, ignore.case = TRUE))
select1 <- GFP$query
z_scores <- calculate_z_scores(annot_tab, select1, vsd)
create_heatmap(z_scores, 
               labels_row = gene_labels[match(select1, gene_labels$query), 2], 
               annotation_col = heatmap_metadata %>% select(Tissue), 
               annotation_colors = ann_colors, 
               filename = "GFP_SwissProt")

Aboral Oral Larvae Paper - Pdam genome

Join our data to Pdam annotations

Annot_Pdam <- read.csv("../references/annotation/blastp_Pdam_out.tab", sep = '\t', header = FALSE) %>% select(c(1,2)) %>% dplyr::rename("protein_id" = "V2", "query" = "V1")
Manual_Pdam <- left_join(Manual, Annot_Pdam) 
library(readxl)
larval_aboral_enriched <- read_excel("../output_RNA/marker_genes/RamonMateu_Pdam_larval.xlsx", sheet = "pocillopora_aboral_enriched_05_")

Manual_Pdam_upaboral <- Manual_Pdam %>% filter(log2FoldChange > 0)
inner_join(larval_aboral_enriched, Manual_Pdam_upaboral, by=c("gene ID"="protein_id")) %>% dim()
## [1] 18 26
#18 genes overlap from the 126 in the paper

larval_oral_enriched <- read_excel("../output_RNA/marker_genes/RamonMateu_Pdam_larval.xlsx", sheet = "pocillopora_oral_enriched_05_lf")

Manual_Pdam_uporal <- Manual_Pdam %>% filter(log2FoldChange < 0)
inner_join(larval_oral_enriched, Manual_Pdam_uporal, by=c("gene ID"="protein_id")) %>% dim()
## [1]  6 26
inner_join(larval_oral_enriched, Manual_Pdam_uporal, by=c("gene ID"="protein_id")) %>% print()
## # A tibble: 6 × 26
##   `gene ID`      baseMean log2FoldChange.x lfcSE.x  stat pvalue.x   padj.x
##   <chr>             <dbl>            <dbl>   <dbl> <dbl>    <dbl>    <dbl>
## 1 XP_027046532.1     88.9             4.29   0.652  5.04 4.54e- 7 8.03e- 5
## 2 XP_027036583.1    563.              3.04   0.287  7.12 1.06e-12 4.11e-10
## 3 XP_027054215.1    918.              2.57   0.275  5.71 1.14e- 8 2.77e- 6
## 4 XP_027054196.1    855.              2.49   0.263  5.65 1.59e- 8 3.76e- 6
## 5 XP_027058731.1   1372.              2.44   0.270  5.35 8.95e- 8 1.85e- 5
## 6 XP_027040620.1   1782.              1.83   0.241  3.45 5.66e- 4 4.98e- 2
## # ℹ 19 more variables: `pfam domain` <chr>, `signal peptide` <chr>,
## #   `astroides ortholog` <chr>, `astroides oral` <chr>,
## #   `clytia ortholog` <chr>, `clytia oral` <chr>, X <int>, query <chr>,
## #   definition <dbl>, log2FoldChange.y <dbl>, lfcSE.y <dbl>, pvalue.y <dbl>,
## #   padj.y <dbl>, Heatmap_Label <chr>, blast_hit <chr>, evalue <dbl>,
## #   ProteinNames <chr>, BiologicalProcess <chr>, GeneOntologyIDs <chr>
#only 6/83 genes overlap


larval_aboral_clusters <- read_excel("../output_RNA/marker_genes/RamonMateu_Pdam_larval_scRNA.xlsx") %>% filter(!is.na(pocillopora))

larval_aboral_clusters_Pacuta <- inner_join(larval_aboral_clusters, Manual_Pdam, by=c("pocillopora"="protein_id"))

#my genes upregulated in oral tissue are high in mucous cells, cool

Updating Renv environment:

After you’ve confirmed your code works as expected, use renv::snapshot() to record the packages and their sources in the lockfile.

renv::snapshot()